Immediate, Scalable Object Category Detection

Yusuf Aytar, Andrew Zisserman
2014 2014 IEEE Conference on Computer Vision and Pattern Recognition  
The objective of this work is object category detection in large scale image datasets in the manner of Video Googlean object category is specified by a HOG classifier template, and retrieval is immediate at run time. We make the following three contributions: (i) a new image representation based on mid-level discriminative patches, that is designed to be suited to immediate object category detection and inverted file indexing; (ii) a sparse representation of a HOG classifier using a set of
more » ... evel discriminative classifier patches; and (iii) a fast method for spatial reranking images on their detections. We evaluate the detection method on the standard PAS-CAL VOC 2007 dataset, together with a 100K image subset of ImageNet, and demonstrate near state of the art detection performance at low ranks whilst maintaining immediate retrieval speeds. Applications are also demonstrated using an exemplar-SVM for pose matched retrieval.
doi:10.1109/cvpr.2014.305 dblp:conf/cvpr/AytarZ14 fatcat:dzk5z5uisrgsbd6ls45oc5jkdq