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Content Based Image Retrieval

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Under Image processing techniques, it describes how we can extract the important part of the image and how can we compare it with the existing technologies. It also describe the future scope of this method

Under Image processing techniques, it describes how we can extract the important part of the image and how can we compare it with the existing technologies. It also describe the future scope of this method

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Content Based Image Retrieval

  1. 1. Rishabh Jamar B053 Rayan Dasoriya B061 Content-Based Image Retrieval
  2. 2. Outline • Introduction • Colourful Descriptors – Columnar Mean – Average RGB method – Color Moments • Results • Comparison • Categories for Retrieval • Techniques for Detecting • Working • Comparison • Inference • Conclusion • References 1
  3. 3. Introduction • Explosive growth of image archive libraries • Many CBIR methods proposed • Works on the basis of similar images available in the database • Colour Descriptor: A fascinating feature 3
  4. 4. Colourful Descriptors 4 Flow Diagram of Three Colourful Descriptors[1]
  5. 5. Columnar Mean • Separate 3-D image colour planes in RGB planes • Calculate row and column mean for each plane • Find average mean to form a feature vector • Compute Euclidean distance • Retrieve Image from database on the basis of least distance vector 5
  6. 6. Average RGB Method • Generate histogram of each plane • Calculate average value of RGB • Compute Euclidean Distance for image similarity • Retrieve images from database having smaller distance to the query image 6
  7. 7. Color Moments Algorithm • Convert RGB to HSV color • For each plane, calculate – Mean – Standard Deviation – Skewness • Store feature vector value obtained through nine moments • Calculate distance • Retrieve images having smallest vector 7
  8. 8. Results • CBIR system deployed for every colour descriptor algorithm • Performance Measure – Precision – Recall – f_measure 8
  9. 9. Comparison 9
  10. 10. 10 Average Precision[1] Average Recall[1] Average f_measure[1]
  11. 11. Categories for Retrieval For the study of Marine Invertebrates: • The taxonomic table containing taxa’s name and nomenclature • The geographical table that includes data of museum catalogue collections or field books • The table on ecology • The table on bibliography 11
  12. 12. Techniques for detecting • Color Moments • Canny-Edge Detection • Wavelet Transform 2
  13. 13. Working 3 Feature fusion CBIR for marine invertebrates[2]
  14. 14. Comparison 4 Average 11 standard precision-recall at 10 graph representation[2]
  15. 15. 5 Fig 1. Cloud usage trends[2] Inference CBIR marine invertebrates user acceptance survey[2]
  16. 16. Conclusion • Abundant of flora and fauna • Proposed method is 98% precise • Allows a query image • Can be used by students for study purpose • Performance calculated using Precision, Recall and f_measure index • Avg. RGB method- More significant 16
  17. 17. Future Scope • Colourful Image Descriptors fused with feature extraction methods like texture for further investigating the accuracy and efficiency. 17
  18. 18. References [1] Kamlesh Kumar,Jian-Ping Li, Zain-ul-abiding, Imran Khan, A Comparative Study AmongColorful Image Descriptors for Content Based Image Retrieval, IEEE, 2016. [2]Mas Rina Mustaffa, Noris Mohd Norowi, and Sim May Yee, Content-based Image Retrieval System for Marine Invertebrates, IEEE, 2016. 12
  19. 19. Any Questions?
  20. 20. Thank You

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