Local Self Similar Descriptors: Comparison and Application to Gastroenterology Images

• Type: Article in International Conference Proceedings Book
• Date: 2014

Authors

Sousa, R (Author) Other; Moura, DC (Author) Other; Mario Dinis Ribeiro (Author) FMUP; Coimbra, M (Author) FCUP

Conference proceedings International

Title: 2014 36TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)

Pages: 4635-4638

36th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

Chicago, IL, AUG 26-30, 2014

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 3 Citations

Other information

Authenticus ID: P-00A-AQC

DOI: 10.1109/embc.2014.6944657

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 4