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Keypoint Reduction for Smart Image Retrieval

Content-based image retrieval (CBIR) is an image retrieval problem with image-content query. This problem is investigated in many applications, such as, human identification, information embedding to real-world objects, life-log, and so on. Through many researches on CBIR, local image features, such as SIFT, SURF, and LBP, defined on image key points are proved to be effective for fast and occlusion-robust image retrieval. In CBIR using local features, it is clear that not all features are necessary for image retrieval. That is, distinctive features have stronger discrimination power than commonly observed features. Also, some local features are fragile against observation distortions. This paper presents an importance measure representing both the robustness and the distinctiveness of a local feature based on diverse density. According to this measure, we can reduce the number of local features related to each database entry. Through some experiments, database having reduced local feature indices performs better than database using all local features as indices.