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Science and technology are highly inheritable undertakings, and any scientific and technological worker can make good progress without the experience and achievements of predecessors or others. In the face of an ever-expanding pool of literature, the ability to efficiently and accurately search for similar works is a major challenge in current research. This paper uses Latent Dirichlet Allocation (LDA) topic model to construct feature vectors for the title and abstract, and the bag-of-words model to construct feature vectors for publication type. The similarity between the feature vectors is measured by calculating the cosine values. The experiment demonstrated that the precision, recall and WSS95 scores of the algorithm proposed in the study were 90.55%, 98.74% and 52.45% under the literature title element, and 91.78%, 99.58% and 62.47% under the literature abstract element, respectively. Under the literature publication type element, the precision, recall and WSS95 scores of the proposed algorithm were 90.77%, 98.05% and 40.14%, respectively. Under the combination of literature title, abstract and publication type elements, the WSS95 score of the proposed algorithm was 79.03%. In summary, the study proposes a robust performance of the literature screening (LS) algorithm based on the LDA topic model, and a similar LS system designed on this basis can effectively improve the efficiency of LS. [ABSTRACT FROM AUTHOR]

Coverless information hiding realizes the hiding of secret messages without modifying the carrier, so being able to resist steganalysis algorithms. Many existing hiding methods, however, still face the dilemma of low hiding capacity (i.e., the volume of hidden messages by a single carrier is bounded), which places some restrictions on the application of the methods. To alleviate this problem, in this paper, we explore an effective coverless information hiding method that can deliver more messages through a cover image. A key step of our method is to construct a visual dictionary based on the bag-of-words model. Utilizing the visual dictionary, one participant can generate a binary coding matrix corresponding to the cover image, and then convert the secret messages, based on the matrix, into a coordinate sequence. To enhance the security, the coordinate sequence is encrypted and the ciphertext is transmitted to another participant together with the cover image. Some experiments are implemented to verify the effectiveness of our method, the results and analysis show that our method performs well in terms of undetectability, hiding capacity, and robustness. Compared with other coverless information hiding methods, our method does not need to construct and maintain a large-scale image library and can deliver more messages by only one single image. These characteristics demonstrate that our method has certain application value, and it also broadens the research ideas of coverless information hiding methods. [ABSTRACT FROM AUTHOR]

In this study, an autonomous underwater vehicle (AUV) detection technique was used as the background to investigate the target classification method for underwater visual images. The target classification method is based on the bag-of-words model, with reduced classification accuracy when the edges of underwater objects are blurred, and when there are many noise points. To solve this problem, an improved visual dictionary generation method was proposed based on the bag-of-words model. The proposed method includes a step on decomposing the complete contour of the target before extracting the features. The DCE (discrete contour evolution) algorithm was used to decompose the complete contour into segments of different lengths, and the SC (shape context) method was applied to extract the features of the contour segments. The FCM (Fuzzy C-Means) algorithm was then used to generate a visual dictionary. To address the classification accuracy problem that causes the underwater targets to decrease after adding spatial location information to the bag-of-words model instead of increasing, an improved spatial location information description method was proposed. The contour segments with the nearest-neighbor relationship were represented as visual phrases, constituting the spatial information of the bag-of-words model. Additionally, for the problem of the shape information of contour segments with different shapes, and the relative position information between the contour segments having different contributions to the target classification, a method was developed to balance the contributions of the two using the optimal weight selected from the experiments. The experimental results demonstrated that the proposed method can effectively improve the classification accuracy of underwater target images. [ABSTRACT FROM AUTHOR]

The research focus in remote sensing scene image classification has been recently shifting towards deep learning (DL) techniques. However, even the state-of-the-art deep-learning-based models have shown limited performance due to the inter-class similarity and the intra-class diversity among scene categories. To alleviate this issue, we propose to explore the spatial dependencies between different image regions and introduce patch-based discriminative learning (PBDL) for remote sensing scene classification. In particular, the proposed method employs multi-level feature learning based on small, medium, and large neighborhood regions to enhance the discriminative power of image representation. To achieve this, image patches are selected through a fixed-size sliding window, and sampling redundancy, a novel concept, is developed to minimize the occurrence of redundant features while sustaining the relevant features for the model. Apart from multi-level learning, we explicitly impose image pyramids to magnify the visual information of the scene images and optimize their positions and scale parameters locally. Motivated by this, a local descriptor is exploited to extract multi-level and multi-scale features that we represent in terms of a codeword histogram by performing k-means clustering. Finally, a simple fusion strategy is proposed to balance the contribution of individual features where the fused features are incorporated into a bidirectional long short-term memory (BiLSTM) network. Experimental results on the NWPU-RESISC45, AID, UC-Merced, and WHU-RS datasets demonstrate that the proposed approach yields significantly higher classification performance in comparison with existing state-of-the-art deep-learning-based methods. [ABSTRACT FROM AUTHOR]

In text categorization, a well-known problem related to document length is that larger term counts in longer documents cause classification algorithms to become biased. The effect of document length can be eliminated by normalizing term counts, thus reducing the bias towards longer documents. This gives us term frequency (TF), which in conjunction with inverse document frequency (IDF) became the most commonly used term weighting scheme to capture the importance of a term in a document and corpus. However, normalization may cause term frequency of a term in a related document to become equal or smaller than its term frequency in an unrelated document, thus perturbing a term's strength from its true worth. In this paper, we solve this problem by introducing a non-linear mapping of term frequency. This alternative to TF is called binned term count (BTC). The newly proposed term frequency factor trims large term counts before normalization, thus moderating the normalization effect on large documents. To investigate the effectiveness of BTC, we compare it against the original TF and its more recently proposed alternative named modified term frequency (MTF). In our experiments, each of these term frequency factors (BTC, TF, and MTF) is combined with four well-known collection frequency factors (IDF), RF, IGM, and MONO and the performance of each of the resulting term weighting schemes is evaluated on three standard datasets (Reuters (R8-21578), 20-Newsgroups, and WebKB) using support vector machines and K-nearest neighbor classifiers. To determine whether BTC is statistically better than TF and MTF, we have applied the paired two-sided t-test on the macro F 1 results. Overall, BTC is found to be 52% statistically significant than TF and MTF. Furthermore, the highest macro F 1 value on the three datasets was achieved by BTC-based term weighting schemes. [ABSTRACT FROM AUTHOR]

To the superiority of low cost and convenience, large-scale Internet of things has been deployed for various monitoring pervasively. Typically cloud platform is utilized as a remote data and control center. However, it is a challenge to the bandwidth and real-time feedback to the server because of mass data with uploading and processing. In this paper, to cope with the above problems, we design a gateway based on edge computing. The gateway not only retains the function of connecting multiple sensors and the interaction between devices and cloud, but also can provide real-time local services for terminal devices. It also can drop abnormal data through sensors' correlation analysis and reduce the amount of data upload by diverting the computing tasks of a server. In this paper, an anomaly detection method based on the bag of words model is introduced. At first, the gateway makes a bag of words representation by the data collected from each sensor, including normal and abnormal data. Then it aggregates these class predictions using the bag of words to calculate the final prediction. Through the simulation analysis based on the data such as humidity, temperature, and lighting monitored by sensors in agricultural greenhouses, the results show that the bag of words model can detect abnormal sensor data. [ABSTRACT FROM AUTHOR]

The research focus in remote sensing scene image classification has been recently shifting towards deep learning (DL) techniques. However, even the state-of-the-art deep-learning-based models have shown limited performance due to the inter-class similarity and the intra-class diversity among scene categories. To alleviate this issue, we propose to explore the spatial dependencies between different image regions and introduce patch-based discriminative learning (PBDL) for remote sensing scene classification. In particular, the proposed method employs multi-level feature learning based on small, medium, and large neighborhood regions to enhance the discriminative power of image representation. To achieve this, image patches are selected through a fixed-size sliding window, and sampling redundancy, a novel concept, is developed to minimize the occurrence of redundant features while sustaining the relevant features for the model. Apart from multi-level learning, we explicitly impose image pyramids to magnify the visual information of the scene images and optimize their positions and scale parameters locally. Motivated by this, a local descriptor is exploited to extract multi-level and multi-scale features that we represent in terms of a codeword histogram by performing k-means clustering. Finally, a simple fusion strategy is proposed to balance the contribution of individual features where the fused features are incorporated into a bidirectional long short-term memory (BiLSTM) network. Experimental results on the NWPU-RESISC45, AID, UC-Merced, and WHU-RS datasets demonstrate that the proposed approach yields significantly higher classification performance in comparison with existing state-of-the-art deep-learning-based methods.

In text categorization, a well-known problem related to document length is that larger term counts in longer documents cause classification algorithms to become biased. The effect of document length can be eliminated by normalizing term counts, thus reducing the bias towards longer documents. This gives us term frequency (TF), which in conjunction with inverse document frequency (IDF) became the most commonly used term weighting scheme to capture the importance of a term in a document and corpus. However, normalization may cause term frequency of a term in a related document to become equal or smaller than its term frequency in an unrelated document, thus perturbing a term’s strength from its true worth. In this paper, we solve this problem by introducing a non-linear mapping of term frequency. This alternative to TF is called binned term count (BTC). The newly proposed term frequency factor trims large term counts before normalization, thus moderating the normalization effect on large documents. To investigate the effectiveness of BTC, we compare it against the original TF and its more recently proposed alternative named modified term frequency (MTF). In our experiments, each of these term frequency factors (BTC, TF, and MTF) is combined with four well-known collection frequency factors (IDF), RF, IGM, and MONO and the performance of each of the resulting term weighting schemes is evaluated on three standard datasets (Reuters (R8-21578), 20-Newsgroups, and WebKB) using support vector machines and K-nearest neighbor classifiers. To determine whether BTC is statistically better than TF and MTF, we have applied the paired two-sided t-test on the macro F1 results. Overall, BTC is found to be 52% statistically significant than TF and MTF. Furthermore, the highest macro F1 value on the three datasets was achieved by BTC-based term weighting schemes.

The depth information of abdominal tissue surface and the position of laparoscope are very important for accurate surgical navigation in computer-aided surgery. It is difficult to determine the lesion location by empirically matching the laparoscopic visual field with the preoperative image, which is easy to cause intraoperative errors. Aiming at the complex abdominal environment, this paper constructs an improved monocular simultaneous localization and mapping (SLAM) system model, which can more accurately and truly reflect the abdominal cavity structure and spatial relationship. Firstly, in order to enhance the contrast between blood vessels and background, the contrast limited adaptive histogram equalization (CLAHE) algorithm is introduced to preprocess abdominal images. Secondly, combined with AKAZE algorithm, the Oriented FAST and Rotated BRIEF(ORB) algorithm is improved to extract the features of abdominal image, which improves the accuracy of extracted symmetry feature points pair and uses the RANSAC algorithm to quickly eliminate the majority of mis-matched pairs. The medical bag-of-words model is used to replace the traditional bag-of-words model to facilitate the comparison of similarity between abdominal images, which has stronger similarity calculation ability and reduces the matching time between the current abdominal image frame and the historical abdominal image frame. Finally, Poisson surface reconstruction is used to transform the point cloud into a triangular mesh surface, and the abdominal cavity texture image is superimposed on the 3D surface described by the mesh to generate the abdominal cavity inner wall texture. The surface of the abdominal cavity 3D model is smooth and has a strong sense of reality. The experimental results show that the improved SLAM system increases the registration accuracy of feature points and the densification, and the visual effect of dense point cloud reconstruction is more realistic for Hamlyn dataset. The 3D reconstruction technology creates a realistic model to identify the blood vessels, nerves and other tissues in the patient’s focal area, enabling three-dimensional visualization of the focal area, facilitating the surgeon’s observation and diagnosis, and digital simulation of the surgical operation to optimize the surgical plan.

We introduce a generic visual descriptor, termed as distribution aware retinal transform (DART), that encodes the structural context using log-polar grids for event cameras. The DART descriptor is applied to four different problems, namely object classification, tracking, detection and feature matching: (1) The DART features are directly employed as local descriptors in a bag-of-words classification framework and testing is carried out on four standard event-based object datasets (N-MNIST, MNIST-DVS, CIFAR10-DVS, NCaltech-101); (2) Extending the classification system, tracking is demonstrated using two key novelties: (i) Statistical bootstrapping is leveraged with online learning for overcoming the low-sample problem during the one-shot learning of the tracker, (ii) Cyclical shifts are induced in the log-polar domain of the DART descriptor to achieve robustness to object scale and rotation variations; (3) To solve the long-term object tracking problem, an object detector is designed using the principle of cluster majority voting. The detection scheme is then combined with the tracker to result in a high intersection-over-union score with augmented ground truth annotations on the publicly available event camera dataset; (4) Finally, the event context encoded by DART greatly simplifies the feature correspondence problem, especially for spatio-temporal slices far apart in time, which has not been explicitly tackled in the event-based vision domain. [ABSTRACT FROM AUTHOR]

Content-based image retrieval (CBIR) states the procedure of recovering images having similar visual content against a query image from image datasets. In CBIR, the selection of redundant and irrelevant features from images results in the semantic gap issue, which occurs during feature representation and machine learning process. The robust image representation for effective and efficient image retrieval mainly depends upon robust feature selection and classification, which also reduces the semantic gap problem of CBIR. This paper proposed an innovative method for effective and efficient CBIR. The method uses sparse complementary features for vigorous image representation, optimal feature selection based on locality-preserving projection, fuzzy c-means clustering, and soft label support vector machine for robust image classification. In CBIR, smaller and larger sizes of codebook improve the recall and precision (accuracy) of the system, respectively. Due to this reason, the proposed method introduces complementary features based on a larger size codebook, which is assembled using two small sizes of codebooks to increase CBIR performance. The three well-known image datasets (i.e. Corel-1000, Corel-1500, and Holidays) are used to assess the performance of the proposed method. The experimental evaluation highlights promising results as compared to recent methods of CBIR. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Traditional Bag-of-Words (BoWs) model is commonly generated with many steps, including local feature extraction, codebook generation and feature quantization, etc. Those steps are relatively independent with each other and are hard to be jointly optimized. Moreover, the dependency on hand-crafted local feature makes BoWs model not effective in conveying high-level semantics. These issues largely hinder the performance of BoWs model in large-scale image applications. To conquer these issues, we propose an End-to-End BoWs (E2BoWs) model based on Deep Convolutional Neural Network (DCNN). Our model takes an image as input, then identifies and separates semantic objects in it, and finally outputs visual words with high semantic discriminative power. Specifically, our model firstly generates Semantic Feature Maps (SFMs) corresponding to different object categories through convolutional layers, then introduces Bag-of-Words Layers (BoWL) to generate visual words from each individual feature map. We also introduce a novel learning algorithm to reinforce the sparsity of the generated E2BoWs model, which further ensures the time and memory efficiency. We evaluate the proposed E2BoWs model on several image search datasets including MNIST, SVHN, CIFAR-10, CIFAR-100, MIRFLICKR-25K and NUS-WIDE. Experimental results show that our method achieves promising accuracy and efficiency compared with recent deep learning based retrieval works. [ABSTRACT FROM AUTHOR]

This paper focuses on the vessel image retrieval from massive data, whose goal is to identify relevant records quickly and accurately when new images are given. Noteworthy, it is necessary to find features with high representativeness under the impact of moisture. Traditional features are extracted on the basis of single convolution feature and manual feature coding. However, only a few can express key features of vessels' images due to the incomplete or redundant information. In order to solve this problem, this paper proposes a new strategy. Two dictionary databases are constructed using different convolution layers in VGG16 network; then they are merged to one database that can strongly express the vessel image. Materially, the combined dictionary database consists of two-layer convolution features, which express the original image well with strengthening key information and less redundant information. The algorithm uses BoW (Bag-of-Words) expression of VGG16 neural network in the domain of image retrieval. Compared with traditional methods using SIFT or SUFT features as BoW, experiments on self-build database shows that the proposed algorithm performs better and achieves higher accuracy. [ABSTRACT FROM AUTHOR]

Signal discrimination contributes to the development of machine–machine and human–machine interactive intelligent systems. In this study, a novel framework for signal discrimination was proposed. The proposed framework comprised three phases. In Phase I, a waveform shape-based feature extraction method was used for parameterizing signals. In Phase II, a novel category-preserving bag-of-words (CPBoW) model was proposed. In Phase III, signals were discriminated using a vector space model with term frequency–inverse document frequency. The bag-of-words model generally demonstrated promising performance for signal discrimination. However, the inherent connections among signals of homogeneous categories were considerably lost during signal framing and codebook generation processes. This was because the codebook was simply generated by clustering signal frame samples in the Euclidean space. In the proposed CPBoW model, Taguchi's quality engineering method was used to develop a category-preserving distance metric for executing a clustering process to generate category-preserving codewords. This preserved category information in the codebook and consequently increased the effectiveness of the discrimination process. The proposed framework was verified through three condition monitoring applications that involved a musical instrument recognition problem, motor bearing fault recognition problem, and heart disease recognition problem. The results indicated the superior performance and effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

In this paper, we design and implement a human-machine interaction application, which enables a visually challenged person to locate and manipulate personal objects in her/his neighborhood. In this setting, we need to develop a tool (embedded in a mobile phone) which is capable of sensing, computing, and guiding the human arm toward the object. This involves solving the following two subproblems: (1) recognition of objects in the input images, and (2) generating control signals, to guide the human for navigation, to reach the desired destination. For the former subproblem, we adapt the bag-of-words framework for recognition and matching on mobile phones. For the latter subproblem, we have developed a moment-based human servoing algorithm which is able to generate commands that help the visually impaired human to localize his hand with respect to the object of interest. All necessary computations take place on the mobile phone. The proposed object recognition and vision-based control design are deployed on a low-/mid-end mobile phone. This can lead to a wide range of applications. With our proposed design and implementation, we demonstrate that our application is effective and accurate, with a high reliability of convergence for different experimental settings. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban) is the property of Zhongguo Xue shu qi Kan (Guang Pan Ban) Dian zi Za zhi She and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the increase of the spatial resolution of aerial images, the shadow problem is more prominent. The shadows affect the applications such as object recognition, image dense matching, and object classification. Existing shadow detection methods can acquire results with high accuracy, but usually need much manual intervention and the traditional stretch models generally lead to color distortion and undercompensation. Therefore, we propose an object-oriented automatic shadow detection method without manual intervention and a shadow compensation method by regional matching. In the proposed method, pixel-based soft shadow detection, which uses Gaussian mixture model to simulate the gray distribution and refines soft shadow map with guiled filtering, is combined with image segmentation result to obtain accurate shadow regions with complete shape and no hole. Then shadow regions are compensated, with less loss of details and brightness imbalance, referring to their optimal homogeneous nonshadow region obtained by regional matching based on Bag-of-Words. The total variation model is used to decrease the noise amplified by the pixel-based stretch and boundary effect in the result. Experiments are performed on three publicly available high-resolution aerial images to demonstrate the superiority of our proposed methods. It shows that the proposed method can accurately detect shadows from urban high-resolution aerial images with an overall rate of over 88%. The compensation results display excellent visual effects compared with the state-of-the-art methods, consistent with the true color of ground objects. [ABSTRACT FROM PUBLISHER]