Content-Based Image Retrieval
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1 Content-Based Image Retrieval Selim Aksoy Department of Computer Engineering Bilkent University
2 Image retrieval Searching a large database for images that match a query: What kind of databases? What kind of queries? What constitutes a match? How do we make such searches efficient? CS 484 Fall Selim Aksoy 2
3 Applications Art Collections Fine Arts Museum of San Francisco Medical Image Databases CT MRI Ultrasound The Visible Human Scientific Databases Earth Sciences General Image Collections for Licensing Corbis Getty Images The World Wide Web Google Microsoft Flickr CS 484 Fall Selim Aksoy 3
4 Corel data set images with annotated keywords CS 484 Fall Selim Aksoy 4
5 Fine Arts Museum of San Francisco images CS 484 Fall Selim Aksoy 5
6 Query formulation Text description (keywords Query by example Query by sketch Symbolic description (man and woman on a beach Relevance feedback CS 484 Fall Selim Aksoy 6
7 Google query on rose CS 484 Fall Selim Aksoy 7
8 Corel query on rose CS 484 Fall Selim Aksoy 8
9 Corbis query on rose CS 484 Fall Selim Aksoy 9
10 Difficulties with keywords Images may not have keywords. (An image is worth how many key-words? Query is not easily satisfied by keywords. A casually dressed couple gazing into each others eyes lovingly with dramatic clouds in the background. Pretty girl doing something active sporty in a summery setting beach - not wearing lycra exercise clothes - more relaxed in tee-shirt. Feature is about deodorant so girl should look active - not sweaty but happy healthy carefree - nothing too posed or set up - nice and natural looking. Content-based image retrieval (CBIR CS 484 Fall Selim Aksoy 10
11 Content-based image retrieval Query Image Retrieved Images User Image Database Distance Measure Images Image Feature Extraction Feature Space CS 484 Fall Selim Aksoy 11
12 Image representations and features Image representations: Iconic Global Region-based Object-based Image features: Color Texture Shape Objects and their relationships (this is the most powerful but you have to be able to recognize the objects! CS 484 Fall Selim Aksoy 12
13 Image similarity Distance measures: Euclidean distance Other L p metrics Histogram intersection Cosine distance Earth mover s distance Probabilistic similarity measures: P( relevance two images P( relevance two images / P( irrelevance two images CS 484 Fall Selim Aksoy 13
14 Global histograms Searching using global color histograms CS 484 Fall Selim Aksoy 14
15 Global histograms Airplanes using color histograms (4/12 Sunsets using Gabor texture (3/12 CS 484 Fall Selim Aksoy 15
16 Query by image content (QBIC CS 484 Fall Selim Aksoy 16
17 Color histograms in QBIC The QBIC color histogram distance is: d T hist (IQ = (h(i - h(q A (h(i - h(q. h(i is a K-bin histogram of a database image. h(q is a K-bin histogram of the query image. A is a K x K similarity matrix. R G B Y C V R G B Y C V ? 1? 1 1 How similar is blue to cyan? CS 484 Fall Selim Aksoy 17
18 Color percentages in QBIC %40 red %30 yellow %10 black CS 484 Fall Selim Aksoy 18
19 Color layout in QBIC CS 484 Fall Selim Aksoy 19
20 Earth mover s distance CS 484 Fall Selim Aksoy 20
21 Earth mover s distance Visualization using EMD and multidimensional scaling CS 484 Fall Selim Aksoy 21
22 Probabilistic similarity measures Two classes: Relevance class A Irrelevance class B Bayes classifier Assign ( i j to Α if P(A ( i j > P(B ( B otherwise Discriminant function for classification P(A ( i j ( i j = = P(B ( i j P(( P(( A P(A B P(B Rank images according to posterior ratio values based on feature differences. i i j j i j CS 484 Fall Selim Aksoy 22
23 Probabilistic similarity measures Residential interiors (12/12 Fields (12/12 CS 484 Fall Selim Aksoy 23
24 Shape-based retrieval CS 484 Fall Selim Aksoy 24
25 Shape-based retrieval CS 484 Fall Selim Aksoy 25
26 Elastic shape matching CS 484 Fall Selim Aksoy 26
27 Iconic matching CS 484 Fall Selim Aksoy 27
28 Iconic matching Wavelet-based image compression Quantization of coefficients CS 484 Fall Selim Aksoy 28
29 Iconic matching CS 484 Fall Selim Aksoy 29
30 Region-based retrieval: Blobworld CS 484 Fall Selim Aksoy 30
31 Region-based retrieval: Blobworld CS 484 Fall Selim Aksoy 31
32 Retrieval using spatial relationships Build graph using regions and their spatial relationships. Similarity is computed using graph matching. image sky above above adjacent tiger inside grass abstract regions above sand above adjacent CS 484 Fall Selim Aksoy 32
33 Combining multiple features Text query on rose CS 484 Fall Selim Aksoy 33
34 Combining multiple features Visual query on CS 484 Fall Selim Aksoy 34
35 Combining multiple features Text query on rose and visual query on CS 484 Fall Selim Aksoy 35
36 Video Google: object matching CS 484 Fall Selim Aksoy 36
37 Video Google Viewpoint invariant descriptors Visual vocabulary CS 484 Fall Selim Aksoy 37
38 Video Google Document 1 Now is the time for all good men to come to the aid of their country. Document 2 Summer has come and passed. The innocent can never last. Inverted index Word Document aid 1 all 1 and 2 can 2 come 1 2 country 1 for 1 good 1 the CS 484 Fall Selim Aksoy 38
39 Video skimming CS 484 Fall Selim Aksoy 39
40 Event detection indexing retrieval CS 484 Fall Selim Aksoy 40
41 Informedia Digital Video Library IDVL interface returned for "El Nino" query along with different multimedia abstractions from certain documents. CS 484 Fall Selim Aksoy 41
42 Informedia Digital Video Library IDVL interface returned for bin ladin" query. The results can be tuned using many classifiers. CS 484 Fall Selim Aksoy 42
43 Relevance feedback In real interactive CBIR systems the user should be allowed to interact with the system to refine the results of a query until he/she is satisfied. CS 484 Fall Selim Aksoy 43
44 Relevance feedback Example methods: Query point movement Query point is moved toward positive examples and moved away from negative examples. Weighting features The CBIR system should automatically adjust the weight that were given by the user for the relevance of previously retrieved documents. Weighting similarity measures Feature density estimation Probabilistic relevance feedback CS 484 Fall Selim Aksoy 44
45 Relevance feedback Positive feedback Ap(A p( p(a Ap(A p( p(a Ap(A p( p(a Ap(A p( p(a K K Bp(B p( p(b Ap(A p( p(a (0 (0 (0 (0 Bp(B p( p(b Ap(A p( p(a (0 (0 (0 (0 Bp(B p( p(b Ap(A p( p(a (0 (2 (2 (0 (0 (2 (2 (0 Bp(B p( p(b Ap(A p( p(a (n-1 (0 (n (n (0 (n-1 (0 (n (n (0 K K K K p(a (n (0 K p(a (2 (n (0 K p(a (3 (2 (n (0 K p(a (m (n K K (1 (0 Negative feedback CS 484 Fall Selim Aksoy 45 Ap(B p( p(b Bp(A p( p(a (n (0 (n (0 (n (0 (n (0 K K K K Ap(B p( p(b Bp(A p( p(a (n (0 (2 (2 (n (0 (n (0 (2 (2 (n (0 K K K K Ap(B p( p(b Bp(A p( p(a (2 (n (0 (3 (3 (2 (n (0 (2 (n (0 (3 (3 (2 (n (0 K K K K Ap(B p( p(b Bp(A p( p(a (m-1 (n (m (m (n (m-1 (n (m (m (n K K K K K K K K (0 (0 (0 (0
46 Relevance feedback Sunsets using color histograms Using combined features (6/12 After 1 st feedback (12/12 (1/12 CS 484 Fall Selim Aksoy 46
47 Relevance feedback Auto racing using color Using combined features (9/12 After 1 st feedback (12/12 histograms (3/12 CS 484 Fall Selim Aksoy 47
48 Indexing for fast retrieval Use of key images and the triangle inequality for efficient retrieval. CS 484 Fall Selim Aksoy 48
49 Indexing for fast retrieval Offline 1. Choose a small set of key images. 2. Store distances from database images to keys. Online (given query Q 1. Compute the distance from Q to each key. 2. Obtain lower bounds on distances to database images. 3. Threshold or return all images in order of lower bounds. CS 484 Fall Selim Aksoy 49
50 Indexing for fast retrieval Hierarchical cellular tree CS 484 Fall Selim Aksoy 50
51 Indexing for fast retrieval CS 484 Fall Selim Aksoy 51
52 Performance evaluation Two traditional measures for retrieval performance in the information retrieval literature are precision and recall. Given a particular number of images retrieved precision is defined as the percentage of retrieved images that are actually relevant and recall is defined as the percentage of relevant images that are retrieved. CS 484 Fall Selim Aksoy 52
53 Current research objective Query Image Retrieved Images User Image Database Images Object-oriented Feature Extraction boat Animals Buildings Office Buildings Houses Transportation Boats Vehicles Categories CS 484 Fall Selim Aksoy 53
54 Demos Blobworld ( Video Google ( research/vgoogle/index.html FIDS ( imagedatabase/demo/fids/ Like Visual Shopping ( Google Image Search ( Yahoo Image Search ( Flickr ( The ESP game ( CS 484 Fall Selim Aksoy 54
55 Vitalas Google Similar Images Demos Google Image Swirl images-with-google-image-swirl.html Microsoft Bing First use keywords then mouse over an image and click on show similar images CS 484 Fall Selim Aksoy 55
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