کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
533406 | 870113 | 2012 | 13 صفحه PDF | دانلود رایگان |

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.
► Proposed an image description method using HLAC feature on posterior probability images.
► The proposed method overcomes the limitation of spatial information of the bag-of-features.
► The proposed method exhibits higher classification performances than the bag-of-features.
Journal: Pattern Recognition - Volume 45, Issue 2, February 2012, Pages 707–719