The utilization of human color categorization for content-based image retrieval

We present the concept of intelligent Content-Based Image Retrieval (iCBIR), which incorporates knowledge concerning human cognition in system development. The present research focuses on the utilization of color categories (or focal colors) for CBIR purposes, in particularly considered to be useful for query-by-heart purposes. However, this research explores its potential use for query-by-example purposes. Their use was validated for the field of CBIR by two experiments (26 subjects; stimuli: 4 times the 216 W3C web-safe colors) and one question ("mention ten colors"). Based on the experimental results a Color LookUp Table (CLUT) was defined. This CLUT was used to segment the HSI color space into the 11 color categories. With that a new color quantization method was introduced making a 11 bin color histogram configuration possible. This was compared with three other histogram configurations of 64, 166, and 4096 bins. Combined with the intersection and the quadratic distance measure we defined seven color matching systems. An experimentally founded benchmark for CBIR systems was implemented (1680 queries were performed measuring relevance and satisfaction). The 11 bin histogram configuration did have an average performance. A promising result since it was a naive implementation and is still a topic of development.