title: Local self similar descriptors: comparison and application to gastroenterology images.
abstract: 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.
authors.forename: Ricardo
authors.initials: R
authors.lastname: Sousa
authors.forename: Daniel C
authors.initials: DC
authors.lastname: Moura
authors.forename: Mario
authors.initials: M
authors.lastname: Dinis-Ribeiro
authors.forename: Miguel T
authors.initials: MT
authors.lastname: Coimbra
mesh_terms: D016099:Endoscopy, Gastrointestinal; D005762:Gastroenterology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*