1. Large-Scale Content-Based Image Retrieval Project Presentation CMPT 880: Large Scale Multimedia Systems and Cloud Computing Under supervision of Dr. Mohamed Hefeeda By: Ahmed Abdelsadek (aabdelsa@sfu.ca)

2. Outlines Introduction Project Scope Work Flow Image Features Indexing and Retrieval Matching Evaluation Conclusion

3. Introduction Current image search engines rely heavily on text to retrieve images ▫User provides keywords, and images having that keyword in the filename or in nearby html are candidates for retrieval. In this project we are willing to try content- based retrieval techniques where the query is an image.

4. Project Scope Similarity using local features. Extracting features from the reference images. Index these features in efficient data structure in a scalable large scale environment Process query images. Search and Match. This project is NOT ▫Recognition, Classification, Categorization

5. Work Flow

6. Image Features Using SIFT features (Scale-invariant feature transform). ▫A SIFT feature is a selected image region (also called keypoint) with an associated descriptor. ▫A SIFT descriptor is a histogram of the image gradients surrounding a keypoint. ▫Using PCA for Dimension Reduction

7. KD-Tree Using KD-Trees ▫Each tree level represent a dimension of a feature ▫Searching the index for the K-nearest neighbours

8. Logical View

9. Physical View

10. Matching For each query we extract the features and then search the index for the K-NN features. For each query feature, each neighbouring feature of it votes to certain image with a score of its rank. The maximum 10 images for the voting array are reported as the most similar images.

11. Evaluation Core KNN ▫Experiments on local machine. ▫Our results vs brute force Image retrieval ▫CalTech, and TRICVID datasets ▫On amazon AWS cloud. ▫We 8 machines.  Dual core  4 GB ram

12. Precision of KNN

13. Scanned Bins Size

14. Affect of Data Size

15. Image Recall @ K

16. First Correct @ K

17. Implementation Details The system is implemented in Java We use Hadoop 1.0.3 We run cloud experiments on AWS services ▫S3 ▫EMR We use some open source libraries ▫For images preprocessing we use : FFMPEG ▫For extracting SIFT features we use : VLFeat

18. Conclusion We implement a full pipeline for image retrieval problem. ▫The framework can easily support different types of features, different indexing methods. We show how we can build a big cloud system from small components.

19. Conclusion Intersection with my research Contributions ▫Feature Selection and Extraction ▫Implement Dimension Reduction ▫Design and Implement Map/Reduce Index ▫Implement Image Matching and Ranking

20. Questions ?

21. Thank you !