Image re ranking system

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Image re ranking system

  1. 1. An Approach to Re-Rank Retrieved Images - Image Search Results with Visual Similarity Presented by Guided by Veningston .K Mr. M. Newlin RajkumarM.E student, Dept of CSE, Lecturer, Dept of CSE,Anna University – Coimbatore, Anna University – Coimbatore, India. India.
  2. 2. Objective To address a ranking problem in web image retrieval System to re-rank images returned by image search engine Re-ranking images by incorporating, visual aspects visual similarity04/17/12 Department of Computer Science and Engineering 2
  3. 3. Introduction Image Retrieval System  Comparative study on text & image based search  Interest point extraction - visual content of images  Re-ranking the results of text based systems using visual information  Finding the largest set of most similar images  Rearranging images based on the similarity04/17/12 Department of Computer Science and Engineering 3
  4. 4. Why image Re-ranking?  To maximize relevancy of image results  To achieve diversity of image results04/17/12 Department of Computer Science and Engineering 4
  5. 5. Proposed Scheme Goal  Retrieve image results that are relevant  Finding common features among images Overview  Interest points on the images are extracted  Similarity of each pair of images are computed  Generate graph model  Apply page ranking04/17/12 Department of Computer Science and Engineering 5
  6. 6. Finding common features amongimages Similarity measurement to handle potential rotation, scale and perspective transformations.04/17/12 Department of Computer Science and Engineering 6
  7. 7. Scale Invariant Feature Transform Image matching and features to similarity04/17/12 Department of Computer Science and Engineering 7
  8. 8. Image similarity Given two images u and v, Corresponding descriptor vector, Du = (d1u, d2u, ...dmu ) and Dv = (d1v, d2v, ...dnv ), Define the similarity between two images simply as the number of interest points shared between two images divided by their average number of interest points.04/17/12 Department of Computer Science and Engineering 8
  9. 9. Graph model Given the visual similarities of the images to be ranked.  Treat images as web documents  Treat similarities as visual hyperlinks  Estimate the probability of images being visited by a user // using page rank  Images with more estimated visits are ranked higher04/17/12 Department of Computer Science and Engineering 9
  10. 10. Similarity to Centrality Centrality – importance of images Given a graph with vertices and a set of weighted edges, define and measure the “importance” of each of the vertices  Vertices = images  Egde weights = similarity A vertex closer to an important vertex should rank higher than others04/17/12 Department of Computer Science and Engineering 10
  11. 11. Similarity to Centrality Ranking scores correspond to probability of arriving in each vertex by traversing through the graph Matrix constructed from the weights of the edges in the Adjacency matrix for graph unweighted graph Decision to take a particular path defined by weighted edges04/17/12 Department of Computer Science and Engineering 11
  12. 12. Page Ranking (PR) A B Determines the order of E search results Method of measuring a C D page’s importance Results are based on Search result Priority this priority order B 3 A 2 C 1 D 1 E 104/17/12 Department of Computer Science and Engineering 12
  13. 13. Overall scheme Computationally infeasible to compute similarities for all images indexed by search engine Pre-cluster web images based on metadata Define the similarity of images Given a query, extract top - N results returned, create graph of visual similarity on the N images Compute image rank only on this subset04/17/12 Department of Computer Science and Engineering 13
  14. 14. Performance metrics  Precision and recall number of relevant images in the returned images  Recall = ---------------------------------------------------------------------- total number of relevant images in the database number of relevant images in the returned images  Precision = --------------------------------------------------------------------- total number of returned images  Result analysis  Screen shots04/17/12 Department of Computer Science and Engineering 14
  15. 15. Merits Minimizing irrelevant images Selecting small set of images Computational cost04/17/12 Department of Computer Science and Engineering 15
  16. 16. Conclusion Ability to reduce the number of irrelevant images A tiny set of important images can be selected from a very large set of candidates04/17/12 Department of Computer Science and Engineering 16
  17. 17. Future work Extensions of this technique to a query driven feature selection.04/17/12 Department of Computer Science and Engineering 17
  18. 18. References [1] Yushi Jing, Shumeet Baluja. VisualRank: Applying PageRank to Large-Scale Image Search. IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol 30, No. 11:1887–1890, 2008. [2] R. Datta, D. Joshi, J. Li, and J. Wang. Image Retrieval: Ideas, Influences, and Trends of the New Age. ACM Computing Surveys, vol. 40, no. 2, 2008. [3] Yushi Jing, Shumeet Baluja. PageRank for Product Image Search. WWW 2008, April 21–25, 2008, Beijing, China. ACM International conf. 2008. [4] K. Mikolajczyk and C. Schmid. A performance evaluation of local descriptors. IEEE Transaction on Pattern Analysis and Machine Intelligence, 27(10):1615–1630, 2005. [5] S. Belongie, J. Malik, and J. Puzicha. Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 24(24):509–522, 2002.04/17/12 Department of Computer Science and Engineering 18
  19. 19. Thank you for your Attention04/17/12 Department of Computer Science and Engineering 19

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