Comparision of color spaces in DCD-based content-based image retrieval systems

Content-based image retrieval (CBIR) is one of the most applicable image processing techniques which includes two main steps: feature extraction and retrieval. A feature vector related to visual contents of image is extracted from the image in the feature extraction step. Three set features color, texture and shape are extracted from image in typical CBIR systems. Dominant color descriptor (DCD) is a method based on color information of the image. There are many color spaces to represent an image, so DCD can be implemented in any of these color spaces. In this paper color spaces RGB, CMY, HSV, CIE Lab, CIE Luv and HMMD are considered and effect of them in DCD features is investigated. Also, the CBIR precision is affected by the number of partitions in DCD method which is analyzed in this paper. Simulation results on Corel-1k dataset show that the HSV color space achieves better precision comparing the other color spaces.