کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
532306 | 869931 | 2013 | 15 صفحه PDF | دانلود رایگان |
In the “bag of visual words (BoVW)” representation each image is represented by an unordered set of visual words. In this paper, a novel approach to encode ordered spatial configurations of visual words in order to add context in the representation is presented. The proposed method introduces a bag of spatio-visual words representation (BoSVW) obtained by clustering of visual words' correlogram ensembles. Specifically, the spherical K-means clustering algorithm is employed accounting for the large dimensionality and the sparsity of the proposed spatio-visual descriptors. Experimental results on four standard datasets show that the proposed method significantly improves a state-of-the-art BoVW model and compares favorably to existing context-based scene classification approaches.
► Reform BoVw representation to include spatio-contextual information.
► Spherical k-means for high-dimentional spatio-visual data clustering.
► Improves a state-of-the-art BoVw model on 4 reference datasets.
► Compares favorably to existing context-based scene classification approaches.
Journal: Pattern Recognition - Volume 46, Issue 3, March 2013, Pages 1039–1053