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Reversible data hiding is a confidential communication technique that takes advantage of image file characteristics, which allows us to hide sensitive data in image files. In this paper, we propose a novel high-fidelity reversible data hiding scheme. Based on the advantage of the multipredictor mechanism, we combine two effective prediction schemes to improve prediction accuracy. In addition, the multihistogram technique is utilized to further improve the image quality of the stego image. Moreover, a model of the grouped knapsack problem is used to speed up the search for the suitable embedding bin in each sub-histogram. Experimental results show that the quality of the stego image of our scheme outperforms state-of-the-art schemes in most cases. [ABSTRACT FROM AUTHOR]

Purpose - This article deals with the matter of significant properties of digital objects and it aims to apply this concept within the NDL. In the text, we present various approaches to the question of significant properties and how to identify them as well as their different kinds. The goal is to try to identify data-centric technical properties of audio and image files within NDL, which need to be preserved and to present a way how to record them. It can be classified as a research study. Design/Methodology/Approach - Regarding used methodology, this article is an analysis. It sums up different approaches of international institutions to the significant properties and the ways of identifying them. It deals more closely with possibilities of recording them in PREMIS and it addresses duplicity of information, too. Building on that, it presents a list of technical properties which we consider significant. Results - The analysis of the technical properties within NDL standards and the significant properties recommended by other institutions allowed identification of the significant properties for audio and image files for single objects within NDL. This list can be used in the future as a groundwork for potential implementation of significant properties to the NDL standards. Originality/Value - The benefit of the article is a comprehensive overview of the matter of significant properties and creation of groundwork for future development of NDL standards. It also mentions other types of significant properties, which need to be addressed in the future. [ABSTRACT FROM AUTHOR]

With the recent advancements in information and communication technologies, the creation and storage of documents has become digitalized. Therefore, many documents are stored on computers. Documents containing personal information can be leaked by internal or external malicious acts, and the problem of information loss for individuals and corporations is gradually increasing. This paper proposes a method to more efficiently and quickly identify the existence of personal information among documents stored in image files on personal and corporate computers to prevent their leakage in advance. We improved the efficiency of personal information detection by classifying optical character recognition (OCR) features by recognition rate and deleting redundant ones to increase detection speed. In addition, the detection time was reduced using the reference frequency of the classified OCR features. Experiments confirm an improvement in the performance of the proposed method compared with that of the existing system. [ABSTRACT FROM AUTHOR]

In the provision of on-demand personalized services in an IoT-based hyper-connected network, it is inevitable for the mobile device that centrally controls personal information to become the focal point. In this IoT environment, because mobile devices serve as a gateway for all personalized services, their protection plays a crucial role in the creation of a secure IoT environment. In the case of Android, the classic mobile platform, security is at risk from repackaging attacks because of structural weaknesses in the platform. To prevent such repackaging attacks, Android-based applications currently utilize various obfuscation techniques and insert tamper detection methods. However, it is possible to easily bypass even these measures. Thus, in this paper we propose an anti-reverse-engineering dynamic tamper detection scheme that applies image steganography to distribute and hide code in PNG image files. We design and implement this proposed scheme, and present the results of a security evaluation of an application with the scheme applied. [ABSTRACT FROM AUTHOR]

In digital era, privacy preservation and data size reduction are important issues and many applications handle them simultaneously. In this paper, authors introduce a novel application of reversible data hiding to protect privacy sensitive region in a color image while reducing its file size. The proposed work introduces entropy as a new performance criterion along with distortion, capacity for reversible data hiding. Evaluation metric of the proposed method is a file size of watermarked and losslessly compressed image. The proposed method preserves privacy and controls rise in image entropy by reversible data hiding. [ABSTRACT FROM AUTHOR]

The current usage of DDI is heterogeneous. It varies over different versions of DDI, different grouping, and unequal interpretation of elements. Therefore, provider of services based on DDI implement complex database models for each developed application, resulting in high costs and application specific and non-reusable models. This paper shows a way to model the binding of DDI to applications in a way that it works independent of most version changes and interpretative differences in a standard like DDI without continuous reimplementation. Based on our DDI-FlatDB approach, shown first at EDDI 2015 & 2016, we present a complete implementation along the use case of a web-based questionnaire editor including sustainable solution for version management and efficient handling of large DDI structures. The user interface is adaptable to different usage scenarios to come. The application supports DDI-Lifecycle from the first question draft and hands over structured (meta-) data to survey institutes and data archives and supports the collaborative questionnaire development for the Pre-election Cross-Section of the German Longitudinal Election Study from early 2017 on. [ABSTRACT FROM AUTHOR]

Image compression leads to minimize the storage-requirement of an image by reducing the size of the image. This paper presents a bit allocation strategy based on psychovisual threshold in image compression considering a similar idea of audio coding. In the audio coding, a dynamic bit allocation to each signal is related to the concept of variable block coding and bit allocation is performed on either a short block or long block of sample signals. Similarity, in our technique, more bits are assigned to a local block with visually-significant low frequency order, and fewer, with visually-insignificant high frequency order. This paper presents a bit allocation strategy based on psychovisual threshold in image compression. A psychovisual threshold is developed by minimizing the visual impact on the image quality degradation in image frequency coding. This paper investigates the error generated by the discrete cosine transform and sets the maximum acceptable error as a psychovisual threshold. The average reconstruction error per pixel on frequency order is utilized to prescribe a set of bit allocations which provide a significant improvement on the quality of image reconstruction at relatively low bit rates. The experimental results show that our dynamic bit-allocation technique in image compression manages to overcome artifact images in the image output. The proposed bit allocation strategy improves the quality of image reconstruction by about 20% compared to JPEG compression. This bit allocation strategy is designed to replace the traditional role of the quantization process in image compression. [ABSTRACT FROM AUTHOR]

Abstract An image database of printed fabrics with repeating dot patterns was created to alleviate issues associated with management of and searches for numerous dot printed fabrics in the printing industry. The function of the database is to archive and allow retrieval of images. First, we discuss image archiving of repeating pattern-based dot printed fabrics. The color image was scanned by resolution of 200 dpi. The wavelet transformation was used to preprocess the image to obtain a scanned image 1/16 of the size of the original to be the stored image. To acquire images with repeating pattern color and repeating pattern template, the binary image of each pattern was obtained using the Sobel edge detection method and a morphological operation. Then pattern elements identical to the target pattern element were screened out. Afterwards, the centroid positions of these identical pattern elements were used to subdivide the repeating pattern color image and repeating pattern template image using a vertical vector method. Finally, the RGB 512-color histogram was used as the color feature of the dot printed fabrics, and the geometric and moment invariant feature values of the repeating pattern template image were used as the pattern feature of the dot printed fabrics. Our experimental results show that images can be acquired that are suitable for use in a dot printed fabric image database. The color and template images of the repeating patterns, which represent the image content of the printed fabrics, were obtained to create an image database of repeating pattern-based dot printed fabrics. This image database contains data on 300 printed fabrics which can be used for subsequent research on database image retrieval. [ABSTRACT FROM AUTHOR]

As digital pathology is becoming more prevalent, the cost of image storage becomes factor to consider. The purpose of this study was to test the effect of reducing the tissue size of frequently scanned slides on digital whole slide image file size and file storage cost. We investigated the file sizes of our most commonly scanned slides – the daily special stain controls. The average file size of these stains ranges from 109 to 476 MB. All of the Masson trichrome slides, Movat pentachrome slides, and PAS slides were reviewed from 2015, and the tissue measurements and file sizes were recorded. New, smaller control tissue blocks were created. Once scanned, the tissue measurements and file sizes were also recorded. Overall, we reduced the size of the tissue on the slide by an average of 74%. We calculate that for these three control tissues alone, $3465 in storage costs could be saved every year by reducing the size of the tissue on the slide. [ABSTRACT FROM PUBLISHER]

Content-based image retrieval (CBIR) scheme has gained popularity in the field of information retrieval for retrieving some relevant images from the image database based on the visual descriptors such as color, texture and/or shape of a given query image. In this paper, color features have been exploited from each color component of an RGB color image by using multiresolution approach since most of the information of an image is undetected at one resolution level while some other undetectable information is visualized in other multi-resolution levels. Initially, Gaussian image pyramid is employed on each color component of the color image and subsequent DCT is computed directly on the obtained multi-resolution image planes. Then some significant DCT coefficients are selected according to the zigzag scanning order. For formation of the feature vector, we have derived some statistical values from AC coefficients and all other DC coefficients are included entirely. Finally, a similarity measure is suggested during image retrieval process and it is found that the overall computation overhead is reduced due to consideration of the proposed similarity measure. The proposed CBIR scheme is validated on a two standard Corel-1K and GHIM-10K image databases and satisfactory results are achieved in terms of precision, recall and F-score. The retrieved results show that the proposed scheme outperforms significantly over other related CBIR schemes. [ABSTRACT FROM AUTHOR]

Nowadays poor security, low transmission and storage efficiency of images have become serious concerns. In order to improve the situation, this paper put forward a new image lossless compression joint encryption algorithm based on chaotic map with all original information intact. The lossless compression uses SPIHT(Set Partitioning in Hierarchical Trees) encoding method based on integer wavelet transform, and encrypt multiple rounds in the process of wavelet coefficients and SPIHT coding applying many kinds of chaotic maps. Experimental results show that the compressed file size is about 50 % of the original file size, which achieves relatively good lossless compression ratio. Besides, the encryption method passes many security tests, such as sensitivity test, entropy test, autocorrelation test, NIST SP800-22 test. There is a high application value in the medical field and the national security department whose image files require a relatively high quality. [ABSTRACT FROM AUTHOR]

This paper focuses on the methodologies to organize and structure image databases. Conventional relational database techniques are optimized to deal with textual and numeric data; however, they are not effective to handle image data. Some progresses have been made in developing new approaches to establish and use image databases, but the applications of these approaches are very labor-intensive, error-prone, and impractical to large-scale databases. In this paper, we propose a new approach to develop the structure of a large-scale image automatically. It is an integrated approach from existing technologies for the new application where the management of image data is focused. In addition, we present a solution to data indexing for the image database with different image types. [ABSTRACT FROM AUTHOR]

»Das Archiv auf Deinem Schreibtisch. Eine Einführung in selbstdokumentierende Bilddateien«. Recent technical developments made it realistic that individual historians could have work stations on their desks, which would allow them access to multi-GB collections of archival documents in digitized form. The problem with such a collection is, that the information systems of different archives usually handle their archival material in such a way, that it becomes very hard to extract content from one of them and integrate them into a local information system optimized to support the research interests of a specific user. An architecture for information system is described, which maximizes the possibility to transfer documents between different such systems. This architecture is based upon the concept of digital objects representing individual documents which contain all the semantic information necessary to integrate them seamlessly into different information systems. [ABSTRACT FROM AUTHOR]

Information represented in a digitized image format is very easy to handle. These image files contain massive amounts of information, which requires efficient storage and transfer methods. There is requirement of investigation of quality issues, transfer methods, and storage mechanisms for such large size of these image files. Image compression requires less storage space for large image files. The transfer time is reduced for compressed files, while moving over networks. The aim of image compression techniques is to reduce the amount of data needed to accurately represent an image, such that this image can be economically transmitted or archived. In the field of medical imaging the use of computers is growing. Every day, a huge amount of data is produced from different medical imaging devices. Storage and transmission of this data becomes a problem, where bandwidth constraints are a major issue. This paper discusses the different types of image compression algorithm and the importance of image compression in medical communication. Some of the basic compression algorithms are used on test data and their performance is tested for wired communication. [ABSTRACT FROM AUTHOR]

This study presents an adaptive segmentation method for pre-processing input data to the patch-based multi-view stereo algorithm. A specially developed greyscale transformation is applied to the input image data, thus redefining the intensity histogram. The Nelder-Mead simplex method is used to adaptively locate an optimised segmentation threshold point in the modified histogram. The transformed input image is then segmented using the acquired threshold value, into foreground and background data. The segmentation information acquired is applied to the initial feature extraction and the cyclic patchexpansion procedure to constrain the reconstruction to a three-dimensional visibility space that excludes background artefacts. The method is targeted at segmenting out potentially disruptive data and is able to realise a reduction in cumulative error of the reconstruction process and thus improve the final reconstruction. With this method, the authors obtain results that are relatively similar to the original patch-based method, but with reduced time and space complexity [ABSTRACT FROM AUTHOR]

During the past years, research has focused on the reconstruction of three-dimensional point cloud models from unordered and uncalibrated sets of images. Most of the proposed solutions rely on the structure-from-motion algorithm, and their performances significantly degrade whenever exchangeable image file format information about focal lengths is missing or corrupted. We propose a preprocessing strategy that permits estimating the focal lengths of a camera more accurately. The basic idea is to cluster the input images into separate subsets according to an array of interpolation-related multimedia forensic clues. This operation permits having a more robust estimate and improving the accuracy of the final model. [ABSTRACT FROM AUTHOR]

This paper describes the effectiveness comparison of noise removal method using visual, semantic and the both features. To automatically generate image dataset from Web, noise image removal should be conducted. Visual and semantic features are available to detect noise images. However, which type of features, and how to combine the two types of feature are unraveled. In this paper, six types of noise image detection method are prepared: the method using visual feature, the method using semantic feature, two methods using both features in parallel and two methods using both features in serial. Through the comparison experiments, it was confirmed that the method that used both visual and semantic features in parallel focusing on noise images: the method showed 77.5% F-measure values. The image dataset with the method would be applied into image recognition in our future. [ABSTRACT FROM AUTHOR]

Mobile virtualization introduces extra layers in software stacks, which leads to performance degradation. Notably, each I/O operation has to pass through several software layers to reach the NAND-flash-based storage systems. This article targets at optimizing I/O for mobile virtualization, since I/O becomes one of major performance bottlenecks that seriously affects the performance of mobile devices. Among all the I/O operations, a large percentage is to update metadata. Frequently updated metadata not only degrade overall I/O performance but also severely reduce flash memory lifetime. In this article, we propose a novel I/O optimization technique to identify the metadata of a guest file system that is stored in a virtual machine image file and frequently updated. Then, these metadata are stored in a small additional non-volatile memory (NVM), which is faster and more endurable to greatly improve flash memory's performance and lifetime. To the best of our knowledge, this is the first work to identify the file system metadata from regular data in a guest OS image file with NVM optimization. The proposed scheme is evaluated on a real hardware embedded platform. The experimental results show that the proposed techniques can improve write performance to 45.21% in mobile devices with virtualization. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method of elaborating and detecting brain tumor from MRI suitable for information sharing via the internet for a healthcare provider. This method allows for reducing image sizes without reducing the information content of the images in terms of detecting tumors. The proposed method involves first clarifying the brain tumor area using a modified K-means clustering method and initial segmentation using mean shift segmentation. Then a threshold setting is used to convert the gray scale image and remove noise by applying an erode operation. Finally, the brain tumors in the images are detected using a watershed algorithm. The proposed method was compared with two well-known methods namely the conventional K-mean clustering and Fuzzy C Means (FCM) clustering. We verified the precision and the objectivity of our proposed method. The average precision and recall for our proposed method were excellent with values of 0.914052 and 0.995641, respectively. Our method detected more brain tumors than the conventional K-means clustering and FCM clustering methods and was able to provide for an efficient image data processing with reduced file sizes. [ABSTRACT FROM AUTHOR]

A skew-estimation method using straight lines in document images is presented. Unlike conventional approaches exploiting the properties of text, we formulate the skew-estimation problem as an estimation task using straight lines in images and focus on robust and accurate line detection. To be precise, we adopt a block-based edge detector followed by a progressive line detector to take clues from a variety of sources such as text lines, boundaries of figures/tables, vertical/horizontal separators, and boundaries of textblocks. Extensive experiments on the datasets of skewed images and competition results reveal that the proposed method works robustly and yields accurate skew-estimation results. [ABSTRACT FROM AUTHOR]

Multimodal imaging systems have recently been drawing attention in fields such as medical imaging, remote sensing, and video surveillance systems. In such systems, estimating depth has become possible due to the promising progress of multimodal matching techniques. We perform a systematic performance evaluation of similarity measures frequently used in the literature for dense multimodal stereovision. The evaluated measures include mutual information (MI), sum of squared distances, normalized cross-correlation, census transform, local self-similarity (LSS) as well as descriptors adopted to multimodal settings, like scale invariant feature transform (SIFT), speeded-up robust features (SURF), histogram of oriented gradients (HOG), binary robust independent elementary features, and fast retina keypoint (FREAK). We evaluate the measures over datasets we generated, compiled, and provided as a benchmark and compare the performances using the Winner Takes All method. The datasets are (1) synthetically modified four popular pairs from the Middlebury Stereo Dataset (namely, Tsukuba, Venus, Cones, and Teddy) and (2) our own multimodal image pairs acquired using the infrared and the electro-optical cameras of a Kinect device. The results show that MI and HOG provide promising results for multimodal imagery, and FREAK, SURF, SIFT, and LSS can be considered as alternatives depending on the multimodality level and the computational complexity requirements of the intended application. [ABSTRACT FROM AUTHOR]

The bag-of-visual-words (BOVW) and Fisher kernel are two popular models in human action recognition, and support vector machine (SVM) is the most commonly used classifier for the two models. We show two kinds of group structures in the feature representation constructed by BOVW and Fisher kernel, respectively, since the structural information of feature representation can be seen as a prior for the classifier and can improve the performance of the classifier, which has been verified in several areas. However, the standard SVM employs L2-norm regularization in its learning procedure, which penalizes each variable individually and cannot express the structural information of feature representation. We replace the L2-norm regularization with group lasso regularization in standard SVM, and a group lasso regularized-support vector machine (GLRSVM) is proposed. Then, we embed the group structural information of feature representation into GLRSVM. Finally, we introduce an algorithm to solve the optimization problem of GLRSVM by alternating directions method of multipliers. The experiments evaluated on KTH, YouTube, and Hollywood2 datasets show that our method achieves promising results and improves the state-of-the-art methods on KTH and YouTube datasets. ©2016 SPIE and IS&T [DOI: 10.1117/1.JEI.25.3.033015] [ABSTRACT FROM AUTHOR]

Variational methods have attracted a lot of attention in the literature, especially for image and mesh segmentation. The methods aim at minimizing the energy to optimize both edge and region detections. We propose a spectral mesh decomposition algorithm to obtain disjoint but meaningful regions of an input mesh. The related optimization problem is nonconvex, and it is very difficult to find a good approximation or global optimum, which represents a challenge in computer vision. We propose an alternating split Bregman algorithm for mesh segmentation, where we extended the image-dedicated model to a three-dimensional (3D) mesh one. By applying our scheme to 3-D mesh segmentation, we obtain fast solvers that can outperform various conventional ones, such as graph-cut and primal dual methods. A consistent evaluation of the proposed method on various public domain 3-D databases for different metrics is elaborated, and a comparison with the state-of-the-art is performed. [ABSTRACT FROM AUTHOR]

Images taken under underwater condition usually have color cast and serious loss of contrast and visibility. Degraded underwater images are inconvenient for observation and analysis. In order to address these problems, an underwater image-enhancement method is proposed. A simple yet effective underwater image color cast removal algorithm is first presented based on the optimization theory. Then, based on the minimum information loss principle and inherent relationship of medium transmission maps of three color channels in an underwater image, an effective visibility restoration algorithm is proposed to recover visibility, contrast, and natural appearance of degraded underwater images. To evaluate the performance of the proposed method, qualitative comparison, quantitative comparison, and color accuracy test are conducted. Experimental results demonstrate that the proposed method can effectively remove color cast, improve contrast and visibility, and recover natural appearance of degraded underwater images. Additionally, the proposed method is comparable to and even better than several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Passive forgery detection aims to detect traces of image tampering without the need for prior information. With the increasing demand for image content protection, passive detection methods able to identify image tampering areas are increasingly needed. However, most current passive approaches either work only for image-level JPEG compression detection and cannot localize region-level forgery, or suffer from high-false detection rates in localizing altered regions. This paper proposes an effective approach based on discrete cosine transform coefficient analysis for the detection and localization of altered regions of JPEG compressed images. This approach can also work with altered JPEG images resaved in JPEG compressed format with different quality factors. Experiments with various tampering methods such as copy-and-paste, image completion, and composite tampering, show that the proposed approach is able to effectively detect and localize altered areas and is not sensitive to image contents such as edges and textures. [ABSTRACT FROM AUTHOR]

Reassembling fragmented image files plays a crucial role in seizing digital evidence from scattered digital image files. The existing algorithms are mainly graph based, which cast the reassembly problem as a K-vertex disjoint path problem in a directed complete graph, which is an NP-complete problem. Based on the padding bytes in BMP files, we present a method to exclude most impossible paths, which can improve the accuracy and decrease the time complexity of the existing graph-based methods. According to the alignment rule of BMP format, padding bytes must be appended to the end of each row to bring up the length of the row to a multiple of 4 bytes. Hence the fragment, being a vertex of the path which correctly reassembles a file, has a property; its byte values at padding positions must be the padding values. Only the fragments with such property can be candidate fragments for the vertex. On the test dataset which is constructed based on 330 image files, taking eight classical methods as examples, we show that the proposed method produces an accuracy improvement ranging from 32% to 55%, and reduces the run time to a scope from 1/6 to 1/237. © 2016 SPIE and IS&T [DOI: 10.1117/1.JEI.25.3.033002] [ABSTRACT FROM AUTHOR]

Academic libraries acquire and steward numeric datasets as well as large collections of image files, audio archives, digital texts, and other non-numeric resources. This article considers how a sample of libraries in the United States, Canada, and the United Kingdom present these collections and make them available for discovery. And, importantly, the article examines whether these non-numeric collections are positioned on the library Website as research datasets, rather than as artifacts of limited local and/or historical interest. Findings from this analysis may help collection managers and Web designers as libraries define their role in connecting users to research data. [ABSTRACT FROM PUBLISHER]

In complementary DNA (cDNA) microarray experiments, the measurement of interest is signal intensity ratio of spots. Each spot have four types of pixel intensities namely red foreground, green foreground, red background and green background. The uncertainty associated with the intensity ratio of a spot depends on the correlations between intensities of these pixels. In this article, we propose a method to estimate correlations between various types of pixel intensities within a spot using a modified form of Moran's I spatial autocorrelation. We estimate these correlations for eight selected spots from image files downloaded from the Gene Expression Omnibus (GEO) database. These estimated correlations are used for finding the uncertainty associated with each of the selected eight spots using the theory of error propagation. We observed that the estimated uncertainty associated with intensity ratio of a spot is less if we consider the correlation between various pixel intensities compared to assuming zero correlation. [ABSTRACT FROM AUTHOR]

This paper proposes a novel reversible image data hiding scheme over encrypted domain. Data embedding is achieved through a public key modulation mechanism, in which access to the secret encryption key is not needed. At the decoder side, a powerful two-class SVM classifier is designed to distinguish encrypted and nonencrypted image patches, allowing us to jointly decode the embedded message and the original image signal. Compared with the state-of-the-art methods, the proposed approach provides higher embedding capacity and is able to perfectly reconstruct the original image as well as the embedded message. Extensive experimental results are provided to validate the superior performance of our scheme. [ABSTRACT FROM AUTHOR]

Thumbnail image is widely used in electronic devices to help the user to scan through original high resolution images. Hence, it is essential to represent the thumbnail image correspondingly to the original image. A blur image should not appear to be a clear image in thumbnail form, where this situation might mislead the perceptual analysis of user. The main purpose of this research work is to develop a downsampling algorithm to create a thumbnail image which includes blur information. The proposed method has three stages involved to obtain the proposed output thumbnail, which are preliminary processes, blur detection, and lastly image downsampling. For preliminary processes, Sobel first-order derivatives, gradient magnitude, and gradient orientation are determined. In blur detection stage, local maximum, local minimum, and gradient orientation are ultilized to calculate the edge width. The thumbnail image with blur information is generated using the average edge width map as a weightage to integrate blur information. This proposed method has achieved satisfying results and has high potential to be applied as one of the thumbnail generation options for photo viewing. [ABSTRACT FROM AUTHOR]

The development of computer security technology is very rapidly. Web security is one of the areas that require particular attention related to the abundance of digital crimes conducted over the web. Unrestricted file upload image is a condition in the process of uploading pictures is not restricted. This can be used to make the attacker retrieve the information that is contained in a system. This research developed with several stages, such as, data collection, analysis of the current conditions, designing improvements to the program code, testing and implementation of the results of patch. Security testing is performed to find out the difference between before and after conditions applied patch unrestricted image file upload. Based on the results of testing done by the method of penetration testing results obtained before the application of patch unrestricted image file upload results respondents said 15% strongly disagree, 85% did not agree. Testing after applying patch unrestricted image file upload results respondents said 7.5% strongly agree, 92.5% agree, so it can be concluded that the development of the patch that has been done has been running smoothly as expected. [ABSTRACT FROM AUTHOR]

Reassembling fragmented image files plays a crucial role in seizing digital evidence from scattered digital image files. However, little attention has been paid to the quality assessment for reassembled image files. We propose a method to assign a reassembled image file with a score indicating its quality, defined as the amount of information that the reassembled image affords for its original image. The score ranges from 0 to 1. A higher score indicates better quality. The basic idea of the proposed method is to disintegrate the quality assessment of a reassembled image file into the calculations of the contributions of the fragments contained in the reassembled image file. For each fragment, if it is not from the original image file, it affords the least contribution. Otherwise its contribution depends on the extent to which the fragment and its neighbor fragments are concatenated correctly. We validate the performance of the proposed method with an extensive subjective study involving 588 reassembled image files. We also show that the existing quality assessments for images cannot be directly used to assess the quality of reassembled image files. [ABSTRACT FROM PUBLISHER]

We propose a robust object cataloging method using multiple locally distinct heterogeneous features for aiding image retrieval. Due to challenges such as variations in object size, orientation, illumination etc. object recognition is extraordinarily challenging problem. In these circumstances, we adapt local interest point detection method which locates prototypical local components in object imageries. In each local component, we exploit heterogeneous features such as gradient-weighted orientation histogram, sum of wavelet responses, histograms using different color spaces etc. and combine these features together to describe each component divergently. A global signature is formed by adapting the concept of bag of feature model which counts frequencies of its local components with respect to words in a dictionary. The proposed method demonstrates its excellence in classifying objects in various complex backgrounds. Our proposed local feature shows classification accuracy of 98% while SURF,SIFT, BRISK and FREAK get 81%, 88%, 84% and 87% respectively. [ABSTRACT FROM AUTHOR]

Internet technology has gone a very far in term of advances and improvements, flash players, video components and other multimedia support are incorporated into the recent web pages. Also, services provided such as: Clouding, storage system, online banking and e-commerce are very common and used on daily basis, still there are many missing components regarding more human interaction with the web page, rather than just seeing and clicking. In this paper, we implemented a system that's capable of identifying mouse location onto a web page and capturing that component (mostly images) extracting its meta-data and Exchangeable Image File Format (EXIF) information, this information is processed further by a basic natural language processing subsystem providing it with text parsing results after tokenizing the string, this is to come with a single conclusion: What dose this image represents. The result is normally a single descriptive word corresponds to that image sent to a micro-controller to be analyzed through a table with corresponding values essentially a set of pulses and signals data, to display it as a smell corresponds to the item under the mouse by applying the pulses to the atomizer to give the user the smell of that object. We found that the system has high successful identification ratio over websites with fairly accurate image identification ratio. [ABSTRACT FROM AUTHOR]

Declustering techniques are widely used in distributed environments to reduce query response time through parallel I/O by splitting large files into several small blocks and then distributing those blocks among multiple storage nodes. Unfortunately, however, many small geospatial image data files cannot be further split for distributed storage. In this paper, we propose a complete theoretical system for the distributed storage of small geospatial image data files based on mining the access patterns of geospatial image data using their historical access log information. First, an algorithm is developed to construct an access correlation matrix based on the analysis of the log information, which reveals the patterns of access to the geospatial image data. Then, a practical heuristic algorithm is developed to determine a reasonable solution based on the access correlation matrix. Finally, a number of comparative experiments are presented, demonstrating that our algorithm displays a higher total parallel access probability than those of other algorithms by approximately 10–15% and that the performance can be further improved by more than 20% by simultaneously applying a copy storage strategy. These experiments show that the algorithm can be applied in distributed environments to help realize parallel I/O and thereby improve system performance. [ABSTRACT FROM AUTHOR]