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Eficient Object Pixel-Level Categorization using Bag of Features

AuthorsAldavert, David; Ramisa, Arnau CSIC ORCID; Toledo, Ricardo; López de Mántaras, Ramón CSIC ORCID
KeywordsObject recognition
Bag of features
Issue Date2009
CitationAdvances in Visual Computing. Lecture Notes in Artificial Intelligence 5875: 44-54 (2009)
AbstractIn this paper we present a pixel-level object categorization method suitable to be applied under real-time constraints. Since pixels are categorized using a bag of features scheme, the major bottleneck of such an approach would be the feature pooling in local histograms of visual words. Therefore, we propose to bypass this time-consuming step and directly obtain the score of a linear Support Vector Machine classiffier. This is achieved by creating an integral image of the components of the SVM which can readily obtain the classification score for any image sub-window with only 10 additions and 2 products, regardless of its size. Besides, we evaluated the performance of two efficient feature quantization methods: the Hierarchical K-Means and the Extremely Randomized Forest. All experiments have been done in the Graz02 database, showing comparable, or even better results to related work with a lower computational cost.
Description5th International Symposium, ISVC 2009, Las Vegas, NV, USA, November 30 - December 2, 2009, Proceedings, Part I. LNAI 5875. Springer
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Appears in Collections:(IIIA) Comunicaciones congresos

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