Unsupervised image ranking

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.

[1]  Shumeet Baluja,et al.  Pagerank for product image search , 2008, WWW.

[2]  Mor Naaman,et al.  Generating diverse and representative image search results for landmarks , 2008, WWW.

[3]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[4]  David A. Forsyth,et al.  Animals on the Web , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[5]  Samy Bengio,et al.  A Discriminative Kernel-Based Approach to Rank Images from Text Queries , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Alexander C. Berg,et al.  Finding iconic images , 2009, 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[7]  Marc'Aurelio Ranzato,et al.  Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Shih-Fu Chang,et al.  Video search reranking via information bottleneck principle , 2006, MM '06.

[9]  Geoffrey E. Hinton,et al.  Semantic hashing , 2009, Int. J. Approx. Reason..

[10]  Antonio Torralba,et al.  Building the gist of a scene: the role of global image features in recognition. , 2006, Progress in brain research.

[11]  G. Griffin,et al.  Caltech-256 Object Category Dataset , 2007 .

[12]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[13]  Gang Wang,et al.  Object image retrieval by exploiting online knowledge resources , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[15]  Jon M. Kleinberg,et al.  Mapping the world's photos , 2009, WWW '09.

[16]  Jian Pei,et al.  Efficiently Answering Top-k Typicality Queries on Large Databases , 2007, VLDB.

[17]  Steven M. Seitz,et al.  Scene Summarization for Online Image Collections , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[18]  Rajat Raina,et al.  Efficient sparse coding algorithms , 2006, NIPS.

[19]  José M. Bioucas-Dias,et al.  A New TwIST: Two-Step Iterative Shrinkage/Thresholding Algorithms for Image Restoration , 2007, IEEE Transactions on Image Processing.

[20]  Antonio Criminisi,et al.  Harvesting Image Databases from the Web , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[21]  Kilian Q. Weinberger,et al.  Resolving tag ambiguity , 2008, ACM Multimedia.

[22]  David G. Stork,et al.  Pattern classification, 2nd Edition , 2000 .

[23]  Geoffrey E. Hinton.  Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.

[24]  Thorsten Joachims,et al.  Optimizing search engines using clickthrough data , 2002, KDD.

[25]  David J. Field,et al.  Sparse coding with an overcomplete basis set: A strategy employed by V1? , 1997, Vision Research.

[26]  Shih-Fu Chang,et al.  Reranking Methods for Visual Search , 2007, IEEE MultiMedia.

[27]  Jan-Michael Frahm,et al.  Modeling and Recognition of Landmark Image Collections Using Iconic Scene Graphs , 2008, International Journal of Computer Vision.

[28]  Marc'Aurelio Ranzato,et al.  Fast Inference in Sparse Coding Algorithms with Applications to Object Recognition , 2010, ArXiv.

[29]  Yang Song,et al.  Tour the world: Building a web-scale landmark recognition engine , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[30]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.