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Multiple object class detection with a generative model

Mikolajczyk, K, Leibe, B and Schiele, B (2006) Multiple object class detection with a generative model Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1. pp. 26-33.

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Abstract

In this paper we propose an approach capable of simultaneous recognition and localization of multiple object classes using a generative model. A novel hierarchical representation allows to represent individual images as well as various objects classes in a single, scale and rotation invariant model. The recognition method is based on a codebook representation where appearance clusters built from edge based features are shared among several object classes. A probabilistic model allows for reliable detection of various objects in the same image. The approach is highly efficient due to fast clustering and matching methods capable of dealing with millions of high dimensional features. The system shows excellent performance on several object categories over a wide range of scales, in-plane rotations, background clutter, and partial occlusions. The performance of the proposed multi-object class detection approach is competitive to state of the art approaches dedicated to a single object class recognition problem. © 2006 IEEE.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
AuthorsEmailORCID
Mikolajczyk, KUNSPECIFIEDUNSPECIFIED
Leibe, BUNSPECIFIEDUNSPECIFIED
Schiele, BUNSPECIFIEDUNSPECIFIED
Date : 2006
Identification Number : 10.1109/CVPR.2006.202
Additional Information : © 2006 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Depositing User : Symplectic Elements
Date Deposited : 15 Oct 2014 16:40
Last Modified : 23 Nov 2014 14:34
URI: http://epubs.surrey.ac.uk/id/eprint/806163

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