A weighted dominant color descriptor for content-based image retrieval

Color has been extensively used in the process of image retrieval. The dominant color descriptor (DCD) that was proposed by MPEG-7 is a famous case in point. It is based on compactly describing the prominent colors of an image or a region. However, this technique suffers from some shortcomings; especially with respect to object-based image retrieval. In this paper, a new semantic feature extracted from dominant colors (weight for each DC) is proposed. The newly proposed technique helps reduce the effect of image background on image matching decision where an object’s colors receive much more focus. In addition, a modification to DC-based similarity measure is also proposed. Experimental results demonstrate that the proposed descriptor with the similarity measure modification performs better than the existing descriptor in content-based image retrieval application. The proposed descriptor considers as step forward to the object-based image retrieval.

► A weighted dominant color descriptor for object-based image retrieval is proposed.
► To alleviate large percentage colors effect, two solutions are proposed.
► Feature-level solution is applied by differentiating background and object colors.
► This semantic feature gives less weight to background color to reduce its effect.
► Mutual color ratio is also proposed as similarity measure-level solution.