Search

"Yuanlianglv(forgiveness-green)" is a neologism about color, its referential meaning refers to green, while its cultural meaning is a kind of joke about being cuckolded. "Yuanlianglv" is not only a compound word but also a newly-emerging color word and a buzzword. According to its triple attributes, this paper expounds on the meaning construction of "yuanlianglv" by conceptual blending theory and analyzes the differences between "yuanlianglv" and common compound color words. Besides, this paper concludes four characteristics of color buzz words: time-sensitivity of sticking to the network hotspots, the internal driving force of socio-cultural background, the social dialect showing group emotions, and harmonious rhythm of syllables.

There are a variety of factors on stage environment such as dramatically changing lights, mutation and similar appearance of performers that make computer vision based performer detection task challenging. Relighting technique is adopted to handle the changing lights problem and can greatly relieve the negative effects caused by changing lights on detection results. Color transfer is one of the commonly used relighting techniques in performer detection task. However, existing color transfer methods will bring artifacts like structural defects, distortions and chromatic noises to processed images that make detection task fail. In this paper, we propose a novel color transfer network to improve the detection results. The proposed network is designed to switch the color distributions between two images. It can either transfers the color style of stage performance to public detection datasets to greatly expand the training datasets, or normalizes the style of captured stage images to some standard template to stable the data distribution. The proposed color transfer network deals with color transfer task in a one-shot framework, i.e. consistently training and predicting, taking advantage of the color name regularization. The effectiveness of the proposed method is validated by experiments on real-captured stage datasets.

Color words were often seen in Chinese poetry and they were always studied with qualitative methods. Encouraged by the previous researches., this paper attempts to calculate the color terms of each period respectively for their PMI values in classical poetry. Then we establish a poetry corpus., which is consist of the representative works of poetry in each period from the Pre-Qin Period to Qing Dynasty. To that end, (1) this paper extracts the prototype color words of the five basic color categories through contrasting the frequency of these color words and (2) by calculating the PMI values of the five basic color words, we find the collocation rules of “white”, “black”, “red”, “yellow”, “cyan” from a diachronic perspective.

This paper aims to find an effective method for identifying colors of fabrics. We consider the following color naming models: Basic Color terms, ISCC-NBS Color system, Frery’s Color name, and Fractals Lab’s Ultimate color vocabulary. The appropriate color name is assigned for a fabric image by using seven types of minimum distance in RGB and HSV color models. Experiments are performed on S3 plain color fabric images. Results show that in general, the method of identifying fabric color name by using Frery’s color name model with the color similarity measure by Euclidean distance in RGB space gives the better result than the other methods. When we analyze in more details by classifying fabric images into three groups based on color saturation, we found that the effective methods for color naming are as follows: Frery’s color name model with the quadratic distance in HSV space works well for the group of low color saturation (96.23% accuracy). Frery’s color name model with Euclidean distance in RGB space works well for the group of medium color saturation (81.82% accuracy). And Fractals Lab’s Ultimate color vocabulary model with the weighted Euclidean distance in HSV space work good for the group of high color saturation (76.09% accuracy).

This paper presents a person re-identification algorithm using HSV color histogram matching and sparse representation. To continuously track a person who is captured by multiple cameras without overlapping regions, the person re-identification is essential. The proposed algorithm uses color information to recognize a person in a low resolution image. The proposed person re-identification algorithm consists of two steps: i) HSV color histogram matching and gallery reduction and ii) sparse representation matching using color name descriptor. The proposed algorithm minimizes unnecessary calculations and increases accuracy by reducing the number of candidates using HSV color histogram matching. In addition, it is invariant to occlusion and illumination variation using the sparse representation-based re-identification approach. As a results, it can be applied to a multiple camera-based video surveillance system for public security in airports and stores.

Person with visual impairment suffers from various problems and is unable to lead a normal regular life because of their blindness. In this paper, the proposal of a wearable device for visually impaired persons is presented that can detect color and speak out the name of the color in two different languages: Bengali and English, through connected headphone. Bluetooth headphone or wire headphone, even speaker can be interfaced with the proposed system. The proposed device will be able to detect color and turn the color name into sound so that visually impaired persons can recognize it. It will be implemented and developed using a microcontroller, color sensor along with an amplifier and wired or Bluetooth connected headphone. The device will employ a reliable microcontroller platform known as Arduino Nano along with Bluetooth module HC-05, color sensor TCS 3200, LM 386 power amplifier with SD card module. The device will be able to decode color information from everyday resources and convert into sound so that blind persons can easily identify any color.

The automation of surveillance systems is important to allow real-time analysis of critical events, crime investigation and prevention. A crucial step in the surveillance systems is the person re-identification (Re-ID) which aims at maintaining the identity of agents in non-overlapping camera networks. Most of the works in literature compare a test sample against the entire gallery, restricting the scalability. We address this problem employing multiple indexing lists obtained by color name descriptors extracted from part-based models using our proposed Predominant Color Name (PCN) indexing structure. PCN is a flexible indexing structure that relates features to gallery images without the need of labelled training images and can be integrated with existing supervised and unsupervised person Re-ID frameworks. Experimental results demonstrate that the proposed approach outperforms indexation based on unsupervised clustering methods such as k-means and c-means. Furthermore, PCN reduces the computational efforts with a minimum performance degradation. For instance, when indexing 50% and 75% of the gallery images, we observed a reduction in AUC curve of 0.01 and 0.08, respectively, when compared to indexing the entire gallery.

This paper introduces new online tools to create fuzzy color spaces and share them with researchers in computational color modeling. Fuzzy color spaces, which are able to fill the semantic gap between the color representation in computers and the subjective human perception, are built according to an approach proposed by the authors in previous works. The methodology for building fuzzy color spaces is based on the paradigm of conceptual spaces including fuzzy boundaries, and considering a collection of color names and corresponding crisp color representatives. In this sense, existing color naming systems in the literature, providing a crisp representative color for each color term, can be employed for building fuzzy color spaces automatically. The fuzzy color spaces are stored in files with a specific format which can be used in JFCS (Java Fuzzy Color Space), a Java open source software for modeling colors developed by the authors. In addition, color naming systems represented in files with a specific format (representative color value and a name for each color term) are also created with these tools. The proposed tools, which are integrated into the website associated to JFCS, enable a functional common repository of fuzzy color spaces as a sharing point to researchers in color modeling.

This paper aims to design and implement a prototype of an affordable talking color identifier for individuals with visual impairment. The proposed device assists the visually impaired in identifying the color of materials and objects by enunciating the color name in four different languages: Thai, English, Chinese, and Vietnamese. An Arduino pro-mini-328 micro-controller is utilized to control the programmable color light-to-frequency converter. An 8 × 8 light emitting diode array on the converter receives object-reflected light, whose irradiance is converted into square wave. The micro-controller then converts the square wave's frequency into a universal RGB code. Once the color code has been obtained, it is compared against the color-coded lookup table, during which the closest color is chosen. MP3 embedded module then selects the pre-recorded MP3 audio file corresponding to the chosen color and decodes the file into an audio signal. Lastly, the audio signal is amplified and sent to the speakers. This device has been assessed by 30 sighted people and 15 visually impaired students. Among the sighted people, the device scores 86.30% in the aspect of design, 64.58% in accuracy, 86.66% in ease of use, and 85.00% in the appropriateness of the price. In contrast, among the visually impaired students, the device scores 100.00% in the aspect of design, 67.92% in accuracy, 100.00% in ease of use, and 100.00% in the appropriateness of the price. The most favored language modes in order of preference are Thai, English, Chinese, and Vietnamese, respectively. Compared to the market alternatives, our device costs 2–3 times lower and is affordable among individuals with low income.

To achieve better performance of visual tracking, an improved TLD tracking algorithm was proposed. Firstly, the performance of objects detection classifiers was improved by the usage of the color attributes of objects. The accuracy of objects detector was boosted by using the color attributes of initial object, which was labeled manually. Secondly, Kalman Filter is adopted to estimate localization of candidate objects. Then, with the help of estimated candidate localizations, the research space of detectors and the complexity of computation were reduced. The improved TLD, called color name TLD, was tested on many Benchmark sequences and the experimental results show that the performance and accuracy outperform other publicly available tracking methods under the different scenes.

Visual tracking is a challenging problem in computer vision. Most state-of-the-art visual trackers either rely on intensity information, texture information, or use simple color representations for image description, which cannot provide allaround invariance to different scene conditions. Meanwhile there exists no single tracking approach that can successfully handle all scenarios. Due to the complexity of the tracking problem, the combination of multiple features should be computationally efficient and possess a certain amount of robustness while maintaining high discriminative power. This paper combine intensity information (cross-bin distribute field, CDF), texture information (enhance histograms of oriented gradients, EHOG) and color information (color name, CN) in a tracking-bydetection framework, in which a simple tracker called CSK is extended for multi-dimension and multi-cue fusion. The proposed approach improves the baseline single-cue tracker by 4.4% in distance precision. Furthermore,we show that our approach achieving 75.4% is better than most recent state-of-the-art tracking algorithms.

In an aging society, a service robot will come into our life. It is important for a robot to identify an object specified by human speech from several objects. Human may request an object for the robot by its name, and/or color name etc. Although there are some research about the method for the object identification based on its name, the object identification based on its color is not discussed enough. In this paper, we propose a method for the object identification by color name. The proposed method detects a saliency based on a color name, and the saliency is used for the identification.

This paper addresses the problem of extracting perceptually dominant color names of images. Our approach is motivated by the principle that the pixels corresponding to one dominant color name identified by human are often context dependent, spatially connected and form a perceptually meaningful region. Our algorithm first learns the probabilistic mapping from a RGB color to a color name. Then, a double-threshold approach is utilized to determine the color name of a RGB pixel in a specific image by considering its neighboring pixels. This scheme effectively deals with the pixels ambiguously belonging to several dominant color names. Last, the saliency information is combined to extract perceptually dominant colors. Experiments on our labeled image data set and the Ebay image set demonstrate the effectiveness of our approach.

In this paper we introduce formal definitions of the concepts of fuzzy color and fuzzy color space. We present an approach to the automatic design of customized fuzzy color spaces on the basis of a collection of crisp colors, each crisp color being fully representative of a certain color term. The approach works on any euclidean crisp space and is based on obtaining a Voronoi diagram having the aforementioned representative crisp colors as centroids. The proposal will be illustrated building fuzzy color spaces on RGB colors on the basis of the ISCC-NBS color naming system.

Paintings have some sensibility information to human hearts. But, the sensibility information will be different to person to person. We like some paintings, while other persons like another ones. For appreciation of paintings, grouping of paintings with similar sensitivity will be helpful. In this paper, we developed a distance measure relation among paintings for similarity grouping. Four similarity relations (types) are developed by the distance measure for classification of paintings. Impression words and color words play an important role for the sensibility expression of paintings. To verify the combination of these words, a self-organizing map(SOM) by two layered neural network, was applied for the grouping of paintings. It was shown that the combination of the impression words and color ones make a well similar grouping of paintings. Grouping similar paintings in impression words and color ones will be useful for a recommendation system.

Performance of traditional content-based image retrieval systems is far from user’s expectation due to the ‘semantic gap’ between low-level visual features and the richness of human semantics. In attempt to reduce the ‘semantic gap’, this paper introduces a region-based image retrieval system with high-level semantic color names. In this system, database images are segmented into color-texture homogeneous regions. For each region, we define a color name as that used in our daily life. In the retrieval process, images containing regions of same color name as that of the query are selected as candidates. These candidate images are further ranked based on their color and texture features. In this way, the system reduces the ‘semantic gap’ between numerical image features and the rich semantics in the user’s mind. Experimental results show that the proposed system provides promising retrieval results with few features used.

This study clarified that a robot tracking a user can influence user decision-making. Two types of robot were used and the robots tracked or did not track the user. A laboratory experiment (n = 118) tested differences in selection ratios corresponding to a color name recommended by the robots. Subjects selected the recommended color name significantly more often when the robot tracked subjects' faces than in the no-recommendation situation, however, when the robot did not track subjects' faces with recommendation, there was no significant difference. The physiological data also showed that the tracking condition generated higher levels of arousal than the non-tracking condition. This suggests that tracking the user's face influences user decision-making.

Color is one of the main visual cues and has been frequently used in image processing, analysis and retrieval. The extraction of high-level color descriptors is an increasingly important problem, as these descriptions often provide link to image content. When combined with image segmentation color naming can be used to select objects by color, describe the appearance of the image and even generate semantic annotations. For example, regions labeled as light blue and strong green may represent sky and grass, vivid colors are typically found in man-made objects, and modifiers such as brownish, grayish and dark convey the impression of the atmosphere in the scene. This paper presents a computational model for color categorization, naming and extraction of color composition. In this work we start from the National Bureau of Standards recommendation for color names, and through subjective experiments develop our color vocabulary and syntax. Next, to attach the color name to an arbitrary input color, we design a perceptually based color naming metric. Finally, we extend the method and develop a scheme for extracting the color composition of a complex image. The algorithm follows the relevant neurophysiological findings and studies on human color categorization. In testing the method the known color regions in different color spaces were identified accurately, the color names assigned to randomly selected colors agreed with human judgments, and the color composition extracted from natural images was consistent with human observations.