Content-based image retrieval using fuzzy class membership and rules based on classifier confidence

Content representation for images with well-defined inter-class boundaries in the feature space remains to be a difficult task. Simple distance-based retrieval (SDR) approaches those operate on the feature space for content-based image retrieval (CBIR) are, therefore claimed to be inefficient by many researchers. Different CBIR approaches have been proposed to surmount the drawbacks of SDR scheme. This study proposes a novel image retrieval scheme. In this scheme, effort is taken to reduce the overall search time of the recently proposed approach called `class membership-based retrieval´ (CMR). The proposed method identifies the confidence in the classification and limits the search to single output class and therefore, reduces the overall search time by 21.76% as compared to CMR. Quantitative methods are proposed to select various parameters used in the algorithm which were computed empirically in the case of earlier approach CMR. The computed parameters are validated using experimental results. The consistent behaviours of the proposed method and earlier methods used in the experiment are demonstrated using different feature sets and distance metrics. While the method can be used as a general purpose image retrieval system, experiment is performed on four texture databases wit different complexities in terms of size, number of texture classes and orientation.