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. (2006) 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 with a benchmark multi way SVM classifier. It is shown that selecting the ROI adds about 5% to the performance and, together with the other improvements, the result is about a 10% improvement over the state of the art for Caltech 256.
2007 IEEE 11th International Conference on Computer Vision