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Learning Image Similarity from Flickr Groups Using Fast Kernel Machines
Nov. 2012 (vol. 34 no. 11)
pp. 2177-2188
Gang Wang, Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore
D. Hoiem, Dept. of Comput. Sci., Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA
D. Forsyth, Dept. of Comput. Sci., Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA
Measuring image similarity is a central topic in computer vision. In this paper, we propose to measure image similarity by learning from the online Flickr image groups. We do so by: Choosing 103 Flickr groups, building a one-versus-all multiclass classifier to classify test images into a group, taking the set of responses of the classifiers as features, calculating the distance between feature vectors to measure image similarity. Experimental results on the Corel dataset and the PASCAL VOC 2007 dataset show that our approach performs better on image matching, retrieval, and classification than using conventional visual features. To build our similarity measure, we need one-versus-all classifiers that are accurate and can be trained quickly on very large quantities of data. We adopt an SVM classifier with a histogram intersection kernel. We describe a novel fast training algorithm for this classifier: the Stochastic Intersection Kernel MAchine (SIKMA) training algorithm. This method can produce a kernel classifier that is more accurate than a linear classifier on tens of thousands of examples in minutes.
Index Terms:
Kernel,Training,Support vector machines,Histograms,Visualization,Feature extraction,Euclidean distance,image organization,Image similarity,kernel machines,stochastic gradient descent,online learning,image classification
Citation:
Gang Wang, D. Hoiem, D. Forsyth, "Learning Image Similarity from Flickr Groups Using Fast Kernel Machines," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 11, pp. 2177-2188, Nov. 2012, doi:10.1109/TPAMI.2012.29
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