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Pooja Viswanathan, Tristram Southey, James Little, and Alan K. Mackworth. Place Classification Using Visual Object Categorization and Global Information. In Proceedings of the Canadian Conference on Computer and Robot Vision, CRV 2011, pp. 1–7, 2011.
Places in an environment are locations where activities occur, and can be described by the objects they contain. This paper discusses the completely automated integration of object detection and global image properties for place classification. We first determine object counts in various place types based on Label Me images, which contain annotations of places and segmented objects. We then train object detectors on some of the most frequently occurring objects. Finally, we use object detection scores as well as global image properties to perform place classification of images. We show that our object-centric method is superior and more generalizable when compared to using global properties in indoor scenes. In addition, we show enhanced performance by combining both methods. We also discuss areas for improvement and the application of this work to informed visual search. Finally, through this work we display the performance of a state-of-the-art technique trained using automatically-acquired labeled object instances (i.e., bounding boxes) to perform place classification of realistic indoor scenes.
@inproceedings{ViswanathanCRV2011,
author = {Pooja Viswanathan and Tristram Southey and James Little and Alan K. Mackworth},
title = {Place Classification Using Visual Object Categorization and Global Information},
booktitle = {Proceedings of the Canadian Conference on Computer and Robot Vision, CRV 2011},
year = {2011},
pages = {1--7},
abstract = {Places in an environment are locations where activities occur, and can be described by the objects they contain. This paper discusses the completely automated integration of object detection and global image properties for place classification. We first determine object counts in various place types based on Label Me images, which contain annotations of places and segmented objects. We then train object detectors on some of the most frequently occurring objects. Finally, we use object detection scores as well as global image properties to perform place classification of images. We show that our object-centric method is superior and more generalizable when compared to using global properties in indoor scenes. In addition, we show enhanced performance by combining both methods. We also discuss areas for improvement and the application of this work to informed visual search. Finally, through this work we display the performance of a state-of-the-art technique trained using automatically-acquired labeled object instances (i.e., bounding boxes) to perform place classification of realistic indoor scenes.},
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