Local self similar descriptors: Comparison and application to gastroenterology images
; Moura, D.C.
; Dinis-Ribeiro, M.
Local self similar descriptors: Comparison and application to gastroenterology images, Proc International Conf. of the IEEE Engineering in Medicine and Biology Society - EMBC, Chicago, IL, United States, Vol. , pp. 4635 - 4638, August, 2014.
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Local descriptors coupled with robust methods for learning visual dictionaries have been a pivotal tool in computer vision. Although the identification of similar patterns is commonly conducted on some stage of the bag-of-words framework, a prior assessment of spatial local similarities can be indicative of specific objects, and thus improved recognition rates. In this work we delve a function of similarity for enhancing the discriminative power of local constrained SIFT descriptors. Motivated by gastrointestinal images where diagnosis through endoscopy plays a decisive role in cancer detection and resulting prognosis, visual cues in these early stages are slim and of difficult perception. In order to capture these patterns we propose a self-similarity approach (based on a neighbourhood analysis of SIFT descriptors) to assess local variances through a weight function. Based on extensive simulations our approach achieved a performance of 88%: 3% higher than the standard SIFT, 10% higher than Haar wavelet and 13% higher than LBPs.