Immediate, scalable object category detection - Aytar, Y; Zisserman, A (IEEE, 2014)
The objective of this work is object category detection in large scale image datasets in the manner of Video Google - an 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 mid-level discriminative classifier patches, and (iii) a fast method for spatial reranking images on their detections. We evaluate the detection method on the standard PASCAL 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.