Spatial Color Indexing and Applications
International Journal of Computer Vision
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color models for outdoor machine vision
Computer Vision and Image Understanding
Combining multi-visual features for efficient indexing in a large image database
The VLDB Journal — The International Journal on Very Large Data Bases
Image Content-Based Retrieval Using Chromaticity Moments
IEEE Transactions on Knowledge and Data Engineering
Robust Histogram Construction from Color Invariants for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Combining spatial and colour information for content based image retrieval
Computer Vision and Image Understanding - Special issue on color for image indexing and retrieval
Pattern Recognition Letters
A Real-time Vision-based Vehicle Tracking and Traffic Surveillance
SNPD '07 Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 01
IEEE Transactions on Circuits and Systems for Video Technology
Video object segmentation using Bayes-based temporal tracking and trajectory-based region merging
IEEE Transactions on Circuits and Systems for Video Technology
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
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For color images, color histograms are generally used as the color feature vectors for classifying the colors of objects. To achieve a higher success rate in color classification, feature vectors with a higher dimension are required, yet this causes a low efficiency with regard to the computation time and memory usage. Therefore, this paper proposes a method of reducing the feature vector dimension by a factor of 170 based on combining two techniques: (i) projecting a color histogram generated in 3D color space into 2D color planes and (ii) converting the color histograms to class histograms using a naive Bayesian classifier. The resulting feature vectors are then classified using a support vector machine method and template matching method to recognize the object colors. With both classification methods, a better recognition rate is achieved than when using the original large feature vectors. Copyright © 2011 John Wiley & Sons, Ltd.