Training products of experts by minimizing contrastive divergence
Neural Computation
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A fast learning algorithm for deep belief nets
Neural Computation
Video search reranking via information bottleneck principle
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Efficiently answering top-k typicality queries on large databases
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Generating diverse and representative image search results for landmarks
Proceedings of the 17th international conference on World Wide Web
Pagerank for product image search
Proceedings of the 17th international conference on World Wide Web
A Discriminative Kernel-Based Approach to Rank Images from Text Queries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reranking Methods for Visual Search
IEEE MultiMedia
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Proceedings of the 18th international conference on World wide web
A New TwIST: Two-Step Iterative Shrinkage/Thresholding Algorithms for Image Restoration
IEEE Transactions on Image Processing
WSMC '09 Proceedings of the 1st workshop on Web-scale multimedia corpus
Unsupervised multi-feature tag relevance learning for social image retrieval
Proceedings of the ACM International Conference on Image and Video Retrieval
A classification based framework for concept summarization
Proceedings of the 20th international conference companion on World wide web
Content quality based image retrieval with multiple instance boost ranking
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Semi-supervised manifold ordinal regression for image ranking
MM '11 Proceedings of the 19th ACM international conference on Multimedia
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In the paper, we propose and test an unsupervised approach for image ranking. Prior solutions are based on image content and the similarity graph connecting images. We generalize this idea by directly estimating the likelihood of each photo in a feature space. We hypothesize the photos at the peaks of this distribution are the most likely photos for any given category and therefore these images are the most representative. Our approach is unsupervised and allows for various feature modalities. We demonstrate the effectiveness of our approach using both visual-content-based and tag-based features. The experimental evaluation shows that the presented model outperforms baseline approaches. Moreover, the performance of our method will only get better with time as more images move online and it is thus possible to build more detailed models based on the approach presented here.