An improved feature vocabulary based method for image categorization

Document Type

Article

Publication Date

5-1-2011

Abstract

The bags of feature and feature vocabulary based approaches have been presented for image categorization due to their simplicity and competitive performance. Some modified versions have been subsequently proposed, incorporating the methods such as adapted vocabularies, fast indexing, and Gaussian mixture models. In this paper, we propose an improvement of replacing the Harris-affine detection method by a random sampling procedure together with an increased number of sample points. Experimental results show that this new method improves categorization accuracy on a five-category problem using the Caltech-4 dataset. It is concluded that random sampling produces higher attainable point density and better categorization performance. © 2011 World Scientific Publishing Company.

Identifier

79957616964 (Scopus)

Publication Title

International Journal of Pattern Recognition and Artificial Intelligence

External Full Text Location

https://doi.org/10.1142/S0218001411008828

ISSN

02180014

First Page

415

Last Page

429

Issue

3

Volume

25

Fund Ref

National Science Foundation

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