Content-Based Image Retrieval using the Bag-of-Words Concept

1. Content-Based Image Retrieval using the Bag-of-Words Concept FatihCakir Melihcan Turk F. Sukru Torun AhmetCagriSimsek

2. Outline • Introduction • Bag-of-Words Concept • Dictionary Formation • Content-Based Image Retrieval using BoW • Results • Conclusion • References

3. Introduction : Motivation • CBIR motivation: Huge amount of multimedia content demands a sophisticated analysis rather than simple textual processing (metadata such as annotations or keywords). • Traditional methods for retrieving images is not very satisfactory or may not meet user demand • E.g. In Google image typing ‘Apple’ returns the Apple products as well as the apple fruit. • Main reason is the ambiguity in the language. Several other limitations.

4. Introduction : Motivation • CBIR systems compensates such issues by analyzing the actual ‘content’ of the image hence yielding a more effective feature for describing the image rather than user defined meta-data • Content may be texture, color or any other information that can be derived from the image itself. • One promising idea is to represents images as ‘words’ analogous to text retrieval solutions. • Document ~ Image, term (word) ~ visual word • First introduces in [3].

5. Object Bag of ‘words’ Bag of ‘words’ Concept

6. China is forecasting a trade surplus of $90bn (£51bn) to $100bn this year, a threefold increase on 2004's $32bn. The Commerce Ministry said the surplus would be created by a predicted 30% jump in exports to $750bn, compared with a 18% rise in imports to $660bn. The figures are likely to further annoy the US, which has long argued that China's exports are unfairly helped by a deliberately undervalued yuan. Beijing agrees the surplus is too high, but says the yuan is only one factor. Bank of China governor Zhou Xiaochuan said the country also needed to do more to boost domestic demand so more goods stayed within the country. China increased the value of the yuan against the dollar by 2.1% in July and permitted it to trade within a narrow band, but the US wants the yuan to be allowed to trade freely. However, Beijing has made it clear that it will take its time and tread carefully before allowing the yuan to rise further in value. China, trade, surplus, commerce, exports, imports, US, yuan, bank, domestic, foreign, increase, trade, value sensory, brain, visual, perception, retinal, cerebral cortex, eye, cell, optical nerve, image Hubel, Wiesel Bag-of-Words Concept: Analogy to documents Of all the sensory impressions proceeding to the brain, the visual experiences are the dominant ones. Our perception of the world around us is based essentially on the messages that reach the brain from our eyes. For a long time it was thought that the retinal image was transmitted point by point to visual centers in the brain; the cerebral cortex was a movie screen, so to speak, upon which the image in the eye was projected. Through the discoveries of Hubel and Wiesel we now know that behind the origin of the visual perception in the brain there is a considerably more complicated course of events. By following the visual impulses along their path to the various cell layers of the optical cortex, Hubel and Wiesel have been able to demonstrate that the message about the image falling on the retina undergoes a of nerve cells stored in columns. In this system each cell has its specific function and is responsible for a specific detail in the pattern of the retinal image.

7. Bag-of-Words Concept: Analogy to documents • Each image can be represented as a histogram . where each bin of the histogram corresponds to a visual word in the dictionary and the value of the bin is the frequency of occurrence of such visual word

8. Bag of ‘words’ Concept • Hence, we consider an image as a document. And as words/terms define a document, visual words define an image. • Words are known? What are ‘visual words’? • Need to define a dictionary

9. codewords dictionary feature detection & representation image representation Bag of ‘words’ Concept : Construct a dictionary 2. 1.

10. … Dictionary Formation: Feature Extraction Represent each patch/interest point with SIFT descriptors [1 Lowe ‘99]

11. … Dictionary Formation : Vector Quantization Vector quantization

12. Example Dictionary

13. Example ‘Visual words’

14. ….. Image Representation frequency codewords

15. Content Based Image Retrieval using BoW • We saw have to represent images using the BoW concept. • With histograms. • It is a mapping of classical text representation onto the image domain. • Hence based on the similarity of histograms we can return ranked results, given an query image. • Category search: Retrieving an arbitrary image representative of a specific class. • Used a subset of Caltech 101 dataset [2].

16. Content Based Image Retrieval using BoW • Given an query image return the top k most similar results. • A ‘positive’ or ‘true’ match considered to be within the same category. • Mean average precision value (MAP) is computed for each category using 10 query images.

17. Content Based Image Retrieval using BoW: Details • For vector quantization K-means is used with K=3000. Hence the dictionary contains 3000 visual words and the histogram has 3000 bins representing each visual word. • L2-norm – Euclidean distance is used for similarity measure. • Visual words are represented using Lowe’s SIFT descriptors. Interest points are extracted using DOG (Difference of Gaussians). • For each of the 18 category 10 query images are used and the average MAP value is considered as the categories success rate.

18. Results : MAP of category based queries

19. Results : MAP of varied dictionary sizes

20. Results • The ‘Motorbikes’ category has the highest MAP rate (0.70). • The lowest is category ‘camera’ (0.07). • Average of MAP rates : 0.25 • As the dictionary size get larger (i.e. more visual words) images are represented accurately, hence MAP values increase • Performance seem to converge after K>3000.

21. Conclusion • Content-Based Image Retrieval systems has gained severe interest among research scientists since multimedia files such as images and videos has dramatically entered our lives throughout the last decade • Textual analysis is not sufficient for effective retrieval systems • Analogous to document representation an image can be described by ‘visual words’. BoW concept. • Using only such feature results are highly satisfying.

22. References • [1] D. G. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 60(2):91–110, 2004 • [2]http://www.vision.caltech.edu/Image_Datasets/Caltech101/ • [3] J. Sivic and A. Zisserman. Video Google: A text retrieval approach to object matching in videos. In Proc. ICCV, 2003

23. Thank You! • Questions and Demo!