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Biomedical Image Retrieval In a Fuzzy Feature Space With Affine Region Detection and Vector Quantization of a Scale-Invariant Descriptor.
This paper presents a biomedical image retrieval approach by detecting affine covariant regions and representing them with an invariant fuzzy feature space. The covariant regions simply refers to a set of pixels or interest points which change covariantly with a class of transformations, such as affinity. A vector descriptor based on Scale-Invariant Feature Transform (SIFT) is then associated with each region, computed from the intensity pattern within the region. The SIFT features are then vector quantized to build a codebbok of keypoints. By mapping the interest points extracted from one image to the keypoints in the codebook, their occurrences are counted and the resulting histogram is called the 'bag of keypoints' for that image. Images are finally represented in fuzzy feature space by spreading each region's membership values through a global fuzzy membership function to all the keypoints in the codebook. The proposed feature extraction and representation scheme is not only invariant to affine transformations but also robust against quantization errors. A systematic evaluation of image retrieval on a biomedical image collection demonstrates the advantages of the proposed image representation approach in terms of precision-recall.