Content aware image retrieval with partition-based color features

In this paper, we present a content aware approach to image retrieval with partitioned color features only. Given a query image, the proposed approach consists of four stages. First, partition the query image into sub-images. Second, calculate the mean of each component in the partitioned sub-images as the color features. Third, find weights for R-, G-, B-component based on their energies for similarity evaluation. Forth, retrieve images in database by a weighted similarity measure. Though the approach is simple, it is effective in image retrieval even with only partitioned color features. The simulation result for the given database indicates that the overall average precision of ten retrieved images is as high as 0.86. Thus the proposed approach can be used in the applications where light computation is sought.