https://www.cs.princeton.edu/courses/archive/spr05/cos598E/bib/rui99_cbir_survey.pdf

Image Retrieval

A novel fuzzy entropy approach to image enhancement and thresholding

Automatic pavement distress detection system

Fuzzy entropy threshold approach to breast cancer detection

Global color histograms are well-known as a simple and often way to perform color-based image retrieval. However, it lacks spatial information about the image colors. The use of a grid of cells superimposed on the images and the use of local color histograms for each such cell improves retrieval in the sense that some notion of color location is taken into account. In such an approach however, retrieval becomes sensitive to image rotation and translation. In this thesis we present a new way to model image similarity, also using colors and a superimposing grid, via bipartite graphs. As a result, the technique is able to take advantage of color location but is not sensitive to rotation and translation. Experimental results have shown the approach to be very effective. If one uses global color histograms as a filter then our approach, named Harbin, becomes quite effcient as well (i.e., it imposes very little overhead over the use of global color histograms).

A Robust CBIR Approach Using Local Color Histograms

A novel image segmentation algorithm derived in a fuzzy entropy framework is presented. First, the fuzzy entropy function is computed based on fuzzy region width and the Shannon's function of the image. Then all of the local entropy maxima are located in order to find the optimal partition for image segmentation scene local entropy maxima corresponding to the uncertainties among various regions in the image. This algorithm is very effective for the images whose histograms have no clear peaks and valleys, or the number of the segmentation classes is unknown, or the probabilistic model of the image and the different segmentation classes are unknown. A large number of experiments have been carried out on different kinds of images. Good performances of the proposed algorithm have been achieved.

X.Q. Li

Papers

A fuzzy logic approach to image segmentation