The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Modern Information Retrieval
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A MFoM learning approach to robust multiclass multi-label text categorization
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Creating Efficient Codebooks for Visual Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Proceedings of the 15th international conference on Multimedia
VisualRank: Applying PageRank to Large-Scale Image Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
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To have a robust and informative image content representation for image categorization, we often need to extract as many as possible visual features at various locations, scales and orientations. Thus it is not surprised that an image has a few hundreds or even thousands of visual descriptors. This raises huge cost of computation and memory. To eliminate the problem, we can only select the most representative and distinctive descriptors and discard the other non-informative features when training the image category models. This paper will present a Markov chain based algorithm to learn a measure of the descriptor importance in order to weigh the degree of representativeness and distinctiveness. From the measures the descriptor selection algorithm is derived. The presented approach starts from constructing a graph with each node being a descriptor to characterize the pair-wise descriptor similarity and then the PageRank algorithm is exploited to estimate the stationary distribution of the graph whose values are the indicator of the descriptor importance. We evaluate the proposed approach on the STOIC-101 landmark dataset. Our experiments demonstrate the Markov chain based descriptor selection can select the most informative descriptors to distinguish the landmarks. Even with the large reduction of the size of descriptors, the classification accuracy is still competitive or overcomes compared with the system without any descriptor selection.