Joint Visual Vocabulary For Animal Classification
2008 (English)In: 19TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOLS 1-6, 2008, 2019-2022 p.Conference paper (Refereed)
This paper presents a method for visual object categorization based on encoding the joint textural information in objects and the surrounding back-ground, and requiring no segmentation during recognition. The framework can be used together with various learning techniques and model representations. Here we use this framework with simple probabilistic models and more complex representations obtained using Support Vector Machines. We prove that our approach provides good recognition performance for complex problems for which some of the existing methods have difficulties. Additionally, we introduce a new extensive database containing realistic images of animals in complex natural environments. We asses the database in a set of experiments in which we compare the performance of our approach with a recently proposed method.
Place, publisher, year, edition, pages
2008. 2019-2022 p.
, INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, ISSN 1051-4651
Complex problems, Existing method, Learning techniques, Model representation, Natural environments, Probabilistic models, Realistic images, Recognition performance, Textural information, Visual objects, Visual vocabularies, Pattern recognition
Computer and Information Science
IdentifiersURN: urn:nbn:se:kth:diva-31231DOI: 10.1109/ICPR.2008.4761710ISI: 000264729001012ScopusID: 2-s2.0-77957947145ISBN: 978-1-4244-2174-9OAI: oai:DiVA.org:kth-31231DiVA: diva2:405906
19th International Conference on Pattern Recognition (ICPR 2008), Tampa, FL, DEC 08-11, 2008
QC 201103242011-03-242011-03-112011-03-24Bibliographically approved