Merged-color histogram for color image retrieval
Ka-Man Wong, Terence Chun-Ho Cheung, Lai-Man Po · Proceedings - International Conference on Image Processing · 2003
Conventional histogram-based image retrieval algorithms usually find only intersecting areas of the color-component distributions of images, and thus work well in matching images with exact colors instead of similar colors, especially for computer generated pictures. This could be greatly affected by overall variations, such as intensity changes. A novel merged-color histogram (MCH) method for color image retrieval is proposed to facilitate matching between similar colors by means of color quantization and palette merging. Color quantization compacts the color information and matches each color instead of color components, and matching of similar colors is accomplished using palette merging. Experimental results show that the proposed MCH method is about 11-32% more precise in the first 20 retrievals for the same image query, and is able to recall 14%-23% more relevant images in the first 100 retrievals, as compared to the conventional RGB-based histogram method.