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ICCV
2007
IEEE

Image Classification using Random Forests and Ferns

9 years 7 months ago
Image Classification using Random Forests and Ferns
We explore the problem of classifying images by the object categories they contain in the case of a large number of object categories. To this end we combine three ingredients: (i) shape and appearance representations that support spatial pyramid matching over a region of interest. This generalizes the representation of Lazebnik et al [16] from an image to a region of interest (ROI), and from appearance (visual words) alone to appearance and local shape (edge distributions). (ii) automatic selection of the regions of interest in training. This provides a method of inhibiting background clutter and adding invariance to the object instance's position, and (iii) the use of random forests (and random ferns) as a multi-way classifier. The advantage of such classifiers (over multi-way SVM for example) is the ease of training and testing. Results are reported for classification of the Caltech-101 and Caltech-256 data sets. We compare the performance of the random forest/ferns classifier...
Andrew Zisserman, Anna Bosch, Xavier Muñoz
Added 14 Oct 2009
Updated 14 Oct 2009
Type Conference
Year 2007
Where ICCV
Authors Andrew Zisserman, Anna Bosch, Xavier Muñoz
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