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Learning to Detect Objects in Images via a Sparse, Part-Based Representation

Summary: Learning to Detect Objects in Images via a
Sparse, Part-Based Representation
Shivani Agarwal, Aatif Awan, and Dan Roth, Member, IEEE Computer Society
Abstract--We study the problem of detecting objects in still, gray-scale images. Our primary focus is the development of a learning-
based approach to the problem that makes use of a sparse, part-based representation. A vocabulary of distinctive object parts is
automatically constructed from a set of sample images of the object class of interest; images are then represented using parts from this
vocabulary, together with spatial relations observed among the parts. Based on this representation, a learning algorithm is used to
automatically learn to detect instances of the object class in new images. The approach can be applied to any object with
distinguishable parts in a relatively fixed spatial configuration; it is evaluated here on difficult sets of real-world images containing side
views of cars, and is seen to successfully detect objects in varying conditions amidst background clutter and mild occlusion. In
evaluating object detection approaches, several important methodological issues arise that have not been satisfactorily addressed in
previous work. A secondary focus of this paper is to highlight these issues and to develop rigorous evaluation standards for the object
detection problem. A critical evaluation of our approach under the proposed standards is presented.
Index Terms--Object detection, image representation, machine learning, evaluation/methodology.

THE development of methods for automatic detection of
objects in images has been a central challenge in
computer vision and pattern analysis research. The main
difficulty in developing a reliable object detection approach


Source: Agarwal, Shivani - Department of Computer Science and Automation, Indian Institute of Science, Bangalore


Collections: Computer Technologies and Information Sciences