A novel histogram-biasing factor for fast sorted histogram-based measurement in large image database retrieval system
The exhaustive histogram matching is usually the most computationally intensive part for any query in most large image database retrieval systems. In this paper, we introduce a histogram-biasing factor (HBF) to measure the biased-behavior of ordered-bins in a sorted histogram. The proposed HBF can be used to increase the early rejection rate of unreliable or impossible candidate reference images based on one of the sorted histograms. Moreover, it can be treated as a color-histogram descriptor. Only images with very closed HBF are taken into account, searching speed can thus be increased without loss of accuracy. Experimental results show that the proposed factor results in up to 13 times speedup meanwhile providing the exhaustive retrieval performance.