Content Based Image Retrieval
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This document outlines techniques for content-based image retrieval (CBIR) using color descriptors. It describes three color descriptor algorithms - columnar mean, average RGB method, and color moments. It evaluates these algorithms on a CBIR system and measures their performance in terms of precision, recall, and f-measure. The average RGB method achieved the highest average precision, recall, and f-measure. The document also discusses applying CBIR for retrieving images of marine invertebrates and compares different techniques for detecting features in images.
![Colourful Descriptors
4
Flow Diagram of Three Colourful Descriptors[1]](https://image.slidesharecdn.com/ippresentation-170502170017/85/Content-Based-Image-Retrieval-4-320.jpg)
![10
Average Precision[1] Average Recall[1]
Average f_measure[1]](https://image.slidesharecdn.com/ippresentation-170502170017/85/Content-Based-Image-Retrieval-10-320.jpg)
![Working
3
Feature fusion CBIR for marine invertebrates[2]](https://image.slidesharecdn.com/ippresentation-170502170017/85/Content-Based-Image-Retrieval-13-320.jpg)
![Comparison
4
Average 11 standard precision-recall at 10 graph representation[2]](https://image.slidesharecdn.com/ippresentation-170502170017/85/Content-Based-Image-Retrieval-14-320.jpg)
![5
Fig 1. Cloud usage trends[2]
Inference
CBIR marine invertebrates user acceptance survey[2]](https://image.slidesharecdn.com/ippresentation-170502170017/85/Content-Based-Image-Retrieval-15-320.jpg)
![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](https://image.slidesharecdn.com/ippresentation-170502170017/85/Content-Based-Image-Retrieval-18-320.jpg)
Content Based Image Retrieval
- 1. Rishabh Jamar B053 Rayan Dasoriya B061 Content-Based Image Retrieval
- 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. 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. Colourful Descriptors 4 Flow Diagram of Three Colourful Descriptors[1]
- 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. 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. 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. Results • CBIR system deployed for every colour descriptor algorithm • Performance Measure – Precision – Recall – f_measure 8
- 9. Comparison 9
- 10. 10 Average Precision[1] Average Recall[1] Average f_measure[1]
- 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. Techniques for detecting • Color Moments • Canny-Edge Detection • Wavelet Transform 2
- 13. Working 3 Feature fusion CBIR for marine invertebrates[2]
- 14. Comparison 4 Average 11 standard precision-recall at 10 graph representation[2]
- 15. 5 Fig 1. Cloud usage trends[2] Inference CBIR marine invertebrates user acceptance survey[2]
- 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. Future Scope • Colourful Image Descriptors fused with feature extraction methods like texture for further investigating the accuracy and efficiency. 17
- 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. Any Questions?
- 20. Thank You