Image representation for generic object recognition using higher-order local autocorrelation features on posterior probability images

Matsukawa Tetsu

Kurita Takio

Pattern Recognition

This paper presents a novel image representation method for generic object recognition by using higher-order local autocorrelations on posterior probability images. The proposed method is an extension of the bag-of-features approach to posterior probability images. The standard bag-of-features approach is approximately thought of as a method that classifies an image to a category whose sum of posterior probabilities on a posterior probability image is maximum. However, by using local autocorrelations of posterior probability images, the proposed method extracts richer information than the standard bag-of-features. Experimental results reveal that the proposed method exhibits higher classification performances than the standard bag-of-features method.

Image recognition

Higher-order local autocorrelation feature

Bag-of-features

Posterior probability image

Information science [ 007 ]

eng

Elsevier Science BV

This is a preprint of an article submitted for consideration in Pattern Recognition (c) 2012 Elsevier B.V. ; Pattern Recognition is available online at ScienceDirect with the open URL of your article;

[ISSN] 0031-3203

[DOI] 10.1016/j.patcog.2011.07.018

[NCID] AA11948832

[DOI] http://dx.doi.org/10.1016/j.patcog.2011.07.018