Fuzzy Logic-Based Image Retrieval

Abstract

Classical mathematic method adopts the rigid logic to measure the similarity of images, and therefore cannot deal with the uncertainty and imprecision exist in the human’s thoughts. This paper imports fuzzy logic method into image retrieval to simulate these properties of human’s thoughts. Different from other researches that also adopt the fuzzy logic method, we emphasis on the followings: (1) adopting the fuzzy language variables to describe the similarity degree of image features, not the features themselves. In this way, we can simulate the nonlinear property of human’s judgments of the image similarity. (2) Making use of the fuzzy inference to instruct the weights assignment among various image features. The fuzzy rules that embed the users’ general perceive of an object guarantee their good robustness to the images of various fields. On the other hand, the user’s subjective intentions can be expressed by the fuzzy rules perfectly. In this paper, we propose a novel shape description method called Minimum Statistical Sum Direction Code (MSSDC). The experiment demonstrates the efficiency and feasibility of our proposed algorithms.