NDLI: Gestalt-inspired features extraction for object category recognition

Content Provider	IEEE Xplore Digital Library
Author	Klavdianos, P. Mansouri, A. Meriaudeau, F.
Copyright Year	2013
Description	Author affiliation: Lab. LE2I, Univ. de Bourgogne, Dijon, France (Mansouri, A.; Meriaudeau, F.) \|\| Dept. of Electron. Eng., Queen Mary Univ. of London, London, UK (Klavdianos, P.)
Abstract	We propose a methodology inspired by Gestalt laws to extract and combine features and we test it on the object category recognition problem. Gestalt is a psycho-visual theory of Perceptual Organization that aims to explain how visual information is organized by our brain. We interpreted its laws of homogeneity and continuation in link with shape and color to devise new features beyond the classical proximity and similarity laws. The shape of the object is analyzed based on its skeleton (good continuation) and as a measure of homogeneity, we propose self-similarity enclosed within shape computed at super-pixel level. Furthermore, we propose a framework to combine these features in different ways and we test it on Caltech 101 database. The results are good and show that such an approach improves objectively the efficiency in the task of object category recognition.
Starting Page	4330
Ending Page	4334
File Size	727188
Page Count	5
File Format	PDF
ISBN	9781479923410
DOI	10.1109/ICIP.2013.6738892
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-15
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Semantic Grouping Region Self-Similarity object category recognition Gestalt
Content Type	Text
Resource Type	Article

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