An Empirical Study of Object Category Recognition: Sequential Testing with Generalized Samples

In this paper we present an empirical study of object category recognition using generalized samples and a set of sequential tests. We study 33 categories, each consisting of a small data set of 30 instances. To increase the amount of training data we have, we use a compositional object model to learn a representation for each category from which we select 30 additional templates with varied appearance from the training set. These samples better span the appearance space and form an augmented training set T of 1980 (60?33) training templates. To perform recognition on a testing image, we use a set of sequential tests to project T into different representation spaces to narrow the number of candidate matches in T . We use"graphlets"(structural elements), as our local features and model T at each stage using histograms of graphlets over categories, histograms of graphlets over object instances, histograms of pairs of graphlets over objects, shape context. Each test is increasi...

• Type: Conference
• Year: 2007
• Where: ICCV
• Authors: Liang Lin, Shaowu Peng, Jake Porway, Song Chun Zhu, Yongtian Wang
• Keywords: Compositional Object Model, Computer Vision, ICCV 2007, Object Category Recognition, Sequential Tests, Small Data Set, Training Set