A New Color Feature Extraction Method Based on Dynamic Color Distribution Entropy of Neighborhoods
This addresses the need for better color features in image retrieval and related tasks, but it appears incremental as it builds on existing entropy methods.
The paper tackled the problem of color feature extraction in image analysis by proposing a dynamic color distribution entropy method that incorporates spatial information, showing acceptable efficiency in image retrieval compared to an improved baseline.
One of the important requirements in image retrieval, indexing, classification, clustering and etc. is extracting efficient features from images. The color feature is one of the most widely used visual features. Use of color histogram is the most common way for representing color feature. One of disadvantage of the color histogram is that it does not take the color spatial distribution into consideration. In this paper dynamic color distribution entropy of neighborhoods method based on color distribution entropy is presented, which effectively describes the spatial information of colors. The image retrieval results in compare to improved color distribution entropy show the acceptable efficiency of this approach.