Content-Based Image Retrieval Benchmarking: Utilizing Color Categories and Color Distributions

From a human centered perspective three ingredients for Content-Based Image Retrieval (CBIR) were developed. First, with their existence confirmed by experimental data, 11 color categories were utilized for CBIR and used as input for a new color space segmentation technique. The complete HSI color space was divided into 11 segments (or bins), resulting in a unique CBIR 11 color quantization scheme. Second, a new weighted similarity function was introduced. It exploits within bin statistics, describing the distribution of color within a bin. Third, a new CBIR benchmark was successfully used to evaluate both new techniques. Based on the 4050 queries judged by the users, the 11 bin color quantization proved to be useful for CBIR purposes. Moreover, the new weighted similarity function significantly improved retrieval performance, according to the users.