|Title||Eficient Object Pixel-Level Categorization using Bag of Features|
|Publication Type||Conference Paper|
|Year of Publication||2009|
|Authors||Aldavert D, Ramisa A, Toledo R, de Mántaras RLópez|
|Conference Name||5th International Symposium on Visual Computing|
In 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 classi- er. This is achieved by creating an integral image of the components of the SVM which can readily obtain the classication score for any image sub-window with only 10 additions and 2 products, regardless of its size. Besides, we evaluated the performance of two ecient feature quantiza- tion 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 compu- tational cost.
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