DocumentCode :
2921067
Title :
Heterogeneous image feature integration via multi-modal spectral clustering
Author :
Cai, Xiao ; Nie, Feiping ; Huang, Heng ; Kamangar, Farhad
Author_Institution :
Comput. Sci. & Eng. Dept., Univ. of Texas at Arlington, Arlington, TX, USA
fYear :
2011
fDate :
20-25 June 2011
Firstpage :
1977
Lastpage :
1984
Abstract :
In recent years, more and more visual descriptors have been proposed to describe objects and scenes appearing in images. Different features describe different aspects of the visual characteristics. How to combine these heterogeneous features has become an increasing critical problem. In this paper, we propose a novel approach to unsupervised integrate such heterogeneous features by performing multi-modal spectral clustering on unlabeled images and unsegmented images. Considering each type of feature as one modal, our new multi-modal spectral clustering (MMSC) algorithm is to learn a commonly shared graph Laplacian matrix by unifying different modals (image features). A non-negative relaxation is also added in our method to improve the robustness and efficiency of image clustering. We applied our MMSC method to integrate five types of popularly used image features, including SIFT, HOG, GIST, LBP, CENTRIST and evaluated the performance by two benchmark data sets: Caltech-101 and MSRC-v1. Compared with existing unsupervised scene and object categorization methods, our approach always achieves superior performances measured by three standard clustering evaluation metrices.
Keywords :
feature extraction; image segmentation; pattern clustering; MMSC; graph Laplacian matrix; image feature integration; image segmentation; multimodal spectral clustering; object categorization methods; visual characteristics; visual descriptors; Clustering algorithms; Computer vision; Histograms; Kernel; Laplace equations; Robustness; Visualization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on
Conference_Location :
Providence, RI
ISSN :
1063-6919
Print_ISBN :
978-1-4577-0394-2
Type :
conf
DOI :
10.1109/CVPR.2011.5995740
Filename :
5995740
Link To Document :
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