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Common-Frame Model for Object Recognition

Moreels, Pierre and Perona, Pietro (2004) Common-Frame Model for Object Recognition. In: Advances in Neural Information Processing Systems 17 (NIPS 2004). Advances in Neural Information Processing Systems. No.17. MIT Press , Cambridge, MA, pp. 953-960. ISBN 0-262-19534-8.

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A generative probabilistic model for objects in images is presented. An object consists of a constellation of features. Feature appearance and pose are modeled probabilistically. Scene images are generated by drawing a set of objects from a given database, with random clutter sprinkled on the remaining image surface. Occlusion is allowed. We study the case where features from the same object share a common reference frame. Moreover, parameters for shape and appearance densities are shared across features. This is to be contrasted with previous work on probabilistic ‘constellation’ models where features depend on each other, and each feature and model have different pose and appearance statistics [1, 2]. These two differences allow us to build models containing hundreds of features, as well as to train each model from a single example. Our model may also be thought of as a probabilistic revisitation of Lowe’s model [3, 4]. We propose an efficient entropy-minimization inference algorithm that constructs the best interpretation of a scene as a collection of objects and clutter. We test our ideas with experiments on two image databases. We compare with Lowe’s algorithm and demonstrate better performance, in particular in presence of large amounts of background clutter.

Item Type:Book Section
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Perona, Pietro0000-0002-7583-5809
Additional Information:© 2005 Massachusetts Institute of Technology.
Series Name:Advances in Neural Information Processing Systems
Issue or Number:17
Record Number:CaltechAUTHORS:20160929-122218111
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Official Citation:Pierre Moreels and Pietro Perona. 2004. Common-frame model for object Recognition. In Proceedings of the 17th International Conference on Neural Information Processing Systems (NIPS'04), L. K. Saul, Y. Weiss, and L. Bottou (Eds.). MIT Press, Cambridge, MA, USA, 953-960.
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:70662
Deposited By: SWORD User
Deposited On:04 Oct 2016 03:27
Last Modified:03 Oct 2019 16:00

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