A New Similarity Measure Based on Fuzzy Monotonic Inclusion for Image Retrieval Systems

This paper proposed a Fuzzy monotonic inclusion (FMI) approach in order to measure similarity between images. Firstly, an image is segmented to several regions, then each region is described by a fuzzy set. Finally, extracted features from each region are mapped into a fuzzy similarity model. FMI scheme makes relation between regions and based on the relations, the regions are selected for the comparison process. Thus, for every image region, both parameters of fuzzy location and area is extracted. We investigated FMI From the conceptual point of view and Semantic relation among Objects. The experimental results on Label Me database, a real world image dataset including 163,000 images, show superiority of the FMI in compared with UFM and Fuzzy Histogram.

Keywords:

Fuzzy similarity , image retrieval , monotonic fuzzy inclusion measure , similarity measure