Title

A Probabilistic Representation For Efficient Large Scale Visual Recognition Tasks

Abstract

In this paper, we present an efficient alternative to the traditional vocabulary based on bag-of-visual words (BoW) used for visual classification tasks. Our representation is both conceptually and computationally superior to the bag-of-visual words: (1) We iteratively generate a Maximum Likelihood estimate of an image given a set of characteristic features in contrast to the BoW methods where an image is represented as a histogram of visual words, (2) We randomly sample a set of characteristic features instead of employing computation-intensive clustering algorithms used during the vocabulary generation step of BoW methods. Our performance compares favorably to the state-of-the-art on experiments over three challenging human action and a scene categorization dataset, demonstrating the universal applicability of our method. © 2011 IEEE.

Publication Date

1-1-2011

Publication Title

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Number of Pages

2593-2600

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/CVPR.2011.5995746

Socpus ID

80052876485 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/80052876485

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