To address the issues of severe missing and false detection rates and application of a fixed dominant color number in state-of-the-art dominant color extraction methods

a histogram peak-filtering and rejection-based algorithm is proposed. The algorithm identifies a small number of dominant colors

with such characteristics as high spatial aggregation degree

highly similar color pixels

small representative error

and large color difference. First

a robust color histogram of the input image is counted by thresholding the spatial aggregation degree of each pixel to avoid the influence of noise. The peaks of the color histogram within a relatively small color range are selected as a candidate dominant color set. Second

the selection color range for the color histogram peaks is progressively increased

and a candidate dominant color filtering process is implemented to reduce similar colors in the candidate dominant color set. When two candidate dominant colors fall in the same selection color range

the one with more similar pixel numbers is preserved. The other one is removed if it contains similar spatial distributions or more common similar pixels; otherwise

it is retained. Finally

the dominant colors are determined through a candidate dominant color rejection process to remove false candidate dominant colors

which are those with a scattered spatial distribution

a few similar pixels in the order of a magnitude

and a small color difference with the others in the candidate dominant color set. In addition

a comprehensive evaluation model is established for the dominant colors of an image. The model can reflect all influencing factors for the dominant colors and avoid the unilateral defects of the traditional evaluation approach. Experimental results prove that the proposed dominant color extraction method is superior as an effective representation of the color features of an image. The average appraisal score is 1.1 times that of the highest score of one of the previous methods; this relatively increases the algorithm's performance by approximately 10%. The proposed algorithm meets the requirement for application in image retrieval

segmentation

editing

etc.