Connecting language to the world
Artificial Intelligence - Special volume on connecting language to the world
A discrete model for color naming
EURASIP Journal on Applied Signal Processing
Combining low-level features for semantic extraction in image retrieval
EURASIP Journal on Advances in Signal Processing
Color correction for multi-view video based on background segmentation and dominant color extraction
WSEAS Transactions on Computers
Color learning and illumination invariance on mobile robots: A survey
Robotics and Autonomous Systems
Connecting language to the world
Artificial Intelligence - Special volume on connecting language to the world
Learning color names for real-world applications
IEEE Transactions on Image Processing
Focusing computational visual attention in multi-modal human-robot interaction
International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction
A Pragmatic Approach for Qualitative Shape and Qualitative Colour Similarity Matching
Proceedings of the 2010 conference on Artificial Intelligence Research and Development: Proceedings of the 13th International Conference of the Catalan Association for Artificial Intelligence
A bayesian network approach to multi-feature based image retrieval
SAMT'06 Proceedings of the First international conference on Semantic and Digital Media Technologies
A model for the qualitative description of images based on visual and spatial features
Computer Vision and Image Understanding
Color naming models for color selection, image editing and palette design
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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The extraction of high-level color descriptors is an increasingly important problem, as these descriptions often provide links 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 generate semantic annotations. This paper presents a computational model for color categorization and naming and extraction of color composition. In this paper, we start from the National Bureau of Standards' recommendation for color names, and through subjective experiments, we develop our color vocabulary and syntax. To assign a color name from the vocabulary to an arbitrary input color, we then design a perceptually based color-naming metric. The proposed algorithm follows relevant neurophysiological findings and studies on human color categorization. Finally, we extend the algorithm and develop a scheme for extracting the color composition of a complex image. According to our results, the proposed method identifies known color regions in different color spaces accurately, the color names assigned to randomly selected colors agree with human judgments, and the description of the color composition of complex scenes is consistent with human observations.