![]() ![]() The BRD is robust to partial matching and histogram normalization, and captures correlations between bins with only a linear computational complexity. We propose a bin ratio-based histogram distance (BRD), which is an intra-cross- bin distance, in contrast with previous bin-to-bin distances and cross- bin distances. In this paper, we deal with this problem by using the ratios between bin values of histograms, rather than bin values' differences which are used in the traditional histogram distances. Large variations in image background may cause partial matching and normalization problems for histogram-based representations, i.e., the histograms of the same category may have bins which are significantly different, and normalization may produce large changes in the differences between corresponding bins. ![]() Hu, Weiming Xie, Nianhua Hu, Ruiguang Ling, Haibin Chen, Qiang Yan, Shuicheng Maybank, Stephen The interplay between these opposing factors effectively implements Occam's razor by selecting the most simple model that best describes the data.īin Ratio-Based Histogram Distances and Their Application to Image Classification. The maximum of the posterior probability occurs at a point where the prior probability and the the joint likelihood are balanced. Opt BINS (optimal binning) determines the optimal number of bins in a uniform bin-width histogram by deriving the posterior probability for the number of bins in a piecewise-constant density model after assigning a multinomial likelihood and a non-informative prior. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |