Adaptive hierarchical density histogram for complex binary image retrieval

This paper proposes a novel binary image descriptor, namely the Adaptive Hierarchical Density Histogram, that can be utilized for complex binary image retrieval. This novel descriptor exploits the distribution of the image points on a two-dimensional area. To reflect effectively this distribution, we propose an adaptive pyramidal decomposition of the image into non-overlapping rectangular regions and the extraction of the density histogram of each region. This hierarchical decomposition algorithm is based on the recursive calculation of geometric centroids. The presented technique is experimentally shown to combine efficient performance, low computational cost and scalability. Comparison with other prevailing approaches demonstrates its high potential.