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Enhancing Image Search Quality Using PageRank Algorithm

This study proposes a method to improve image search quality by rearranging Google search results based on image diversity and relevance. The application of PageRank algorithm in image retrieval is explored, aiming to provide more satisfactory and diverse image results. Experimental results demonstrate the effectiveness of the proposed approach. The research highlights the importance of image diversity and quality in search engine results. Despite some limitations in diversity discussion, the method offers a novel and practical way to enhance image retrieval.

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Enhancing Image Search Quality Using PageRank Algorithm

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  1. VisualRank- Applying PageRank to Large-Scale Image Search Presenter：Chien-Hsing Chen Author: Yushi Jing ShumeetBaluja 2008.PAMI (IEEE Transactions on Pattern Analysis and Machine Intelligence)

  2. Outline • Motivation • Objective • Method • Experiments • Conclusion • Comment

  3. Motivation • retrieved images may be not fitting (satisfactory) or not diverse How the image could be retrieved ? The news shows a disappointed salesman of Coca Colareturns from his Middle East assignment. A friend asked, “Why weren’t you successful with the Arabs?”

  4. Example 2

  5. Objective • improve quality of image retrieval by rearrange the results of Google search engine • incorrect retrieval • d80 Coca Cola • diversity (retrieved images should be different) • You should know: • 1. adjacency matrix, matrix product • 2. eigenvector, PageRank()

  6. Main idea 1/2 0.6 • Rearrange • previous works • Query-to-images • in this paper • Images-to-images 0.9 0.4 0.3 0.2 0.1 0.1

  7. Main idea 2/2 • Rearrange • Images-to-images • 1. • similar local features • Web site source • my homepage V.S. Yahoo • 2. • diversity 0.6 0.9 0.4 0.3 0.1 0.2 0.1

  8. Images-to-images 1/2 x4 x5 x1 x2 x3 x1 x2 x3 Top ranked images : x4 x5 How to connect between vertexes ? (how to build edge sets) Adjacency matrix

  9. Images-to-images 2/2 x4 x5 x1 x2 x3 x1 x2 x3 x4 Top ranked images : x5 How to give the scores between vertexes ? Adjacency matrix

  10. How to connect between vertexes ? Common local features 1/2 • which pair has most number of common (similar) local features? (a) local features, such as hands, eyes, are similar (g) local features are very different

  11. Common local features 2/2

  12. [n × n] matrix eigenvector image relationship • Which image has most number of common (similar) local features? • A image of which features are similar to the features in the other images. The image is important. × ＝ The entry is evaluated by “local features” uniform ?

  13. PageRank PageRank() concerns the properties of “Hub” and “Authority” Web sites appearing in front of the Google responds are more important than that appearing in back of the ones. d 8 9 11 3 100

  14. image diversity Top ranked images with respect to diversity:

  15. Experiments

  16. c

  17. c

  18. Conclusion • arrange the images from the results of Google search engine

  19. Comment • Advantage • The aspect is novel and easy to implement. • Drawback • less discussion in diversity • Application • responds of search engine • an option is to cluster the resulted images

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