Real-Time Object Segmentation Using a Bag of Features Approach

Authors:
David Aldavert;Arnau Ramisa;Ramon López de Mántaras;Ricardo Toledo
Affiliations:
Computer Vision Center, Dept. Ciencies de la Computació, Universitat Autonòma de Barcelona, Catalunya, Spain;INRIA-Grenoble, LEAR Team, France and Institut d'Investigació d'Inteligencia Artificial (IIIA-CSIC), Campus UAB, Catalunya, Spain;Institut d'Investigació d'Inteligencia Artificial (IIIA-CSIC), Campus UAB, Catalunya, Spain;Computer Vision Center, Dept. Ciencies de la Computació, Universitat Autonòma de Barcelona, Catalunya, Spain
Venue:
Proceedings of the 2010 conference on Artificial Intelligence Research and Development: Proceedings of the 13th International Conference of the Catalan Association for Artificial Intelligence
Year:
2010

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Abstract

In this paper, we propose an object segmentation framework, based on the popular bag of features (BoF), which can process several images per second while achieving a good segmentation accuracy assigning an object category to every pixel of the image. We propose an efficient color descriptor to complement the information obtained by a typical gradient-based local descriptor. Results show that color proves to be a useful cue to increase the segmentation accuracy, specially in large homogeneous regions. Then, we extend the Hierarchical K-Means codebook using the recently proposed Vector of Locally Aggregated Descriptors method. Finally, we show that the BoF method can be easily parallelized since it is applied locally, thus the time necessary to process an image is further reduced. The performance of the proposed method is evaluated in the standard PASCAL 2007 Segmentation Challenge object segmentation dataset.