Keyblock Approach: Metadata Generation and Retrieval of Geographic Imagery

1. University at Buffalo The State University of New York 07.25. 2001 Keyblock Approach: Metadata Generation and Retrieval of Geographic Imagery Aidong Zhang Associate Professor Director, Multimedia and Database Laboratory Computer Science and Engineering University at Buffalo

2. Introduction • Observations: • USGS, NIMA and NASA provide the archiving of large repositories of remote-sensing data. • New Issues: problem of resource selection. Given a query, where should a user start a search? • Our Approach: • Design a metaserver on top of various visual databases. • Given a query, the metaserver first produces a ranking of the databse sites and then distributes the queries to the selected databases.

3. Distributed System Architecture GIS database at remote sites GIS Database GIS Database GIS Database GIS Database Metaserver Metaserver (Our focus) Metasearch Agent Meta Database Query Manager Client applications for visual display Client Browser Client Browser Client Browser Client Browser

4. GIS1998 Server/DB GIS1999 Server/DB GIS2000 Server/DB GISWNY Server/DB Step 1 Local Severs/DB Users METASEVER /DB Ranked DB List 1.GIS-SANF Server/DB 2.GIS-1999 Server/DB …… 7.GIS-FLOR Server/DB Local Severs/DB Step 2 GIS-SANF Server/DB GIS-FLOR Server/DB GIS-FLOR2 Server/DB Matching Images

5. Global View of Data Sources METADATABASE Feature Classes Texture Color Shape Templates DB1 DB2 Database Sites DBn

6. Generating Templates Images are clustered and the centroids of the clusters are chosen as templates. Environment Residential Water Grass Agriculture

7. Metadatabase • Templates of local databases are collected in the metadatabaseto represent the content of the databases • Statistical data: • We can measure the similarity of images in the databases to the templates. • Using these similarity measurements, statistical data are computed that capture the likelihood of a database containing data that are relevant to a template. • The relevant databases for a given query can be selected by determining the similarity of the query with metadatabase templates and ranking the database sites based on the visual relationships recorded between the databases and templates.

8. Content-based Image Retrieval (CBIR) • Allow retrievals performed on various of image contents such as color, texture, shape, etc. • Visual queries are submitted to image database to find similar images • Feature extraction is the basis of CBIR • Famous systems include QBIC, VisualSeek, PhotoBook, etc.

9. Evaluation Measures • Effectiveness of CBIR set_of_retrieved images set_of_relevant images

10. Multi-scale Feature Representation Multi-resolution wavelet representation of image: Original image Scale 1 Scale 2 Scale 3

12. Keyblock Approach • Generalizing text retrieval techniques to image retrieval • Text IR: use keywords to index and retrieve • What are the “keywords” of an image? • Region segments of images • Features of images • Objects of images • How to generate “keywords” of images? • Keyblocks: select centroids of clusters

13. Keyblock Generation Image Database Sampling Training Blocks Feature-based Clustering (GLA,PNNA,etc.) Codebook Training Set Query Image Image Encoding Content-based Image Retrieval Feature Representation: BM, VM, HM, etc. Query and Retrieval

14. Keyblock Generation • Various clustering algorithms can be used. • On original space partition/segment the images into smaller blocks, and then select a subset of representative blocks. • On feature space extract low-level feature vectors, such as color, texture, and shape, from image segments/blocks, and then select a subset of representative feature vectors.

15. Unsupervised Keyblock Selection Step 1: Initialization Step 2: Clustering/Partition Step 3: Recalculating Centroid Step 4: Substituting Centroid and Reiterating

16. Knowledge-based Keyblock Generation Training Images Training Images Stage I Keyblock Generation Keyblock Generation (Forest) (Water) Forest Codebook Water Codebook Merge Codebooks Stage II LVQ-based Fine Tuning Stage III

17. Image Encoding • For each image in the database, decompose it into blocks. • Then, for each block, find the closest entry in the codebook and store the index correspondingly. • Now each image is a matrix of indices, which can be regarded as 1-dimensional in scan order. This property is very similar to a text document which is considered as a linear list of keywords in text-based IR.

18. Codebook ( a list of keyblocks) 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 …... Block Encoding Table Lookup Segmentation 18 16 16 16 15 15 18 18 16 16 18 18 Segmented Image 19 19 19 16 Original Image Encoded Image Image Decoding Reconstructed Image

19. A raw image and the reconstructed images with different codebooks

20. Image Feature Representation and Retrieval • Main components: • the list of encoded images. • list of keyblocks. • the CBIR model • f is the feature extraction mapping which generates the feature vector for each image ; • s is the similarity measure between feature vectors. It is used to generate the ranking in the retrieval stage. • the set of visual queries.

21. Single-block Models • Boolean Model and Vector Model are widely used in IR • adopt keywords to index and retrieve documents; • assume that both documents in the database and queries can be described by a set of mutually independent keywords. • Similar image feature representation models can be designed • use keyblocks instead of keywords for images; • individual keyblock's appearance in images is important information.

22. Boolean Model • BM considers whether or not a keyblock appears. • Wij = 1 if fij >= T,0 otherwise. • fij is the frequency of keyblock ci appearing in image Ij , T is a threshold. • The feature vectors of Ij and q can be considered as strings of length N where i-th bit indicates whether or not ci appears. • SBM (q,dj ) = n11 * w11 + n00 * w00 • n11 is the number of bits at which both Ijand q are 1 • n00 is the number of bits at which both Ijand q are 0 • w11 and w00 are the weights assigned to n11 and n00 , respectively.

23. Vector Model • normalized frequency • inverse image frequency idfi = log( M / Mi) ,for ci • keyblock weights: wij = f*ij * idfi • Similarity measure is the inner product of Ij and q

24. Histogram Model • HM can be regarded as a special case of VM where wij = fij. • The feature vectors Ij and q are the keyblock histograms. • Similarity measure where

25. N-block Models • The single-block models only focus on individual keyblock’s appearance, the correlation among keyblocks are not counted in. • We propose N-block Models • the correlation of image blocks is the focus. • the probabilities of a subset of keyblocks distributed according to certain spatial configurations are used as feature vectors.

26. Bi-block Spatial Configurations horizontal vertical c c c k-1 k k-1 c k diagonal (minor) diagonal (main) c c k-1 k-1 c c k k

27. Tri-block Spatial Configurations horizontal vertical c k-2 c c c c k-2 k-1 k k-1 c k diagonal (main) diagonal (minor) c c k-2 k-2 c c k-1 k-1 c c k k

28. Tri-block Spatial Configurations triangular configure 4 triangular configure 1 c c c k-2 k-2 k-1 c c c k-1 k k triangular configure 2 triangular configure 3 c c c k k k-1 c c c k-2 k-2 k-1

29. Multi-modal Image Retrieval • The above models capture different image content under various contexts. • The single-block models only consider single keyblock's occurrence; • The n-block models consider multiple keyblocks' co-occurrence. • If keyblocks of different size are used, image content in different granularity will be focused on. • Since each individual model can't satisfy all requirements of image content extraction and retrieval, it is necessary to combine them to improve the retrieval performance. • Feature combination • Result fusion

30. keyblock-keyblock correlation matrix • keyblock-keyblock correlation matrix • The rows and columns are associated with the keyblocks in the codebook C (|C| = N) • Each item (ki,l) isa normalized correlation factor between keyblock ciand cl • niis the number of images which contain ci; • nlis the number of images which contain cl; • ni,lis the number of images which contain both ci and cl

31. keyblock-keyblock correlation matrix • We can use the keyblock-keyblock correlation matrix to redefine the feature vector of the histogram model • fijis the frequency of keyblock ciappearing in image Ijand • fij*is the correlation weight calculated by combining frequencies of ci’s correlated keyblocks with their correlation factor together. •  is a threshold (usually 0.3 0.5 ) to cut off the effects of those less correlated keyblocks.

32. Region-based Image Retrieval • Keyblocks • can be any image feature segments such as pixels, blocks and regions, etc. • Regions • Are better “keywords” because they usually carry more semantic meanings and they are closer to the objects . • Image segmentation is still a difficult problem.Segmentation algorithms inevitably make some mistakes, e.g., over-segmentation. • How to effectively and efficiently extract region features? • How to retrieve images based on region features and corresponding region spatial constraints?

33. Region-based Image Retrieval • Images are segmented into several regions; • Visual features are extracted for each region; • The image content is represented by the set of region features; • At the query time, the query image is segmented into several regions. Then the features of one or more regions are matched against region features which represent images in the database.

34. Integrate Regions into Keyblock Framework • Keyblock framework is quite extensible; substitute blocks with regions in the whole framework • Segmentation : Expectation-Maximization (EM) • proposed in the Blobworld system • iteratively models the joint distribution of color and texture with a mixture of Gaussians • Region features • Color feature: color histogram of the pixels in the region. based on the original keyblock representation (1x1, 128); • Texture feature: the mean texture contrast and anisotropy of the pixels in the region; • Normalized area feature: the number of pixels of a region divided by the image size.

35. Integrate Regions into Keyblock Framework • Shape features: X-axis and Y-axis profiles (10-dimension feature vector ) • (1) Find the minimum bounding box B of the region; • (2) Equally subdivide B along both X and Y axes into 5 intervals; • (3) For each cell (u,v) obtained from the above subdivision, calculate the percentage p(u,v) of the region that cell (u,v) contains; • (4) Define the profile of the region along the X-axis as a 5-element array x with the i-th element x(i) = 5v=1 p(i,v); • (5) Similarly define the profile of the region along the Y-axis as a 5-element array y with the j-th element y(j) = 5v=1p(u,j).

36. Feature Combination Model • In the phase of feature extraction, for each image, combine feature vectors generated by different models into one comprehensive feature vector. • Feature vectors • Model  • Model • Combination Model  where or

37. Result Fusion Model • In the phase of retrieval, for each image, combine retrieval results under different models. • <image, similarity> lists • Model  • Model • Combination Model  where

38. Experiments on Test Databases • CDB (web color images) • 500 images , 41 groups, each group 10 or 20 images • 41 training images are randomly selected • query set : whole database • color feature techniques: histogram and color coherent vector (CCV) • average precision and recall from 1 to 40 returned images are calculated. • TDB (Brodatz texture images) • 2240 images , 112 groups, each group 20 images • 112 training images are randomly selected • query set : whole database • texture feature techniques : haar and daubechies wavelet • average precision and recall from 1 to 40 returned images are calculated.

39. Experiments: comparison with traditional techniques

40. Performance of N-block Models All the three n-block models achieve higher performance than the traditional techniques, while the bi-block and uni-block models perform better on these two datasets.

41. Experiments on COREL • 31646 color images • size 120x80 or 80x120 • 939 training images are randomly selected to get keyblocks • query set • 6895 query images which are categorized to 82 groups. • average precision and recall from 1 to 100 returned images are calculated.

42. Experiments on COREL -- Performance Comparison • The performance of the keyblock approach outperforms the traditional techniques.

43. Experiments on GEO • Database GEO • Airphoto images of the Buffalo area provided by NCGIA at Buffalo • 405 images • 46 training images are used to get keyblocks • Query set • 33 query images which are sub-images of 32 x 32 chosen from the images in the database by GIS experts from NCGIA at Buffalo. • These query images are divided into 5 categories: agriculture, grass, forest, residential area, and water.

44. Experiments on GEO : comparison with wavelet transforms

45. An Example Query

46. Experiments for Region-based Image Retrieval • Data set with 1004 images (14 categories) • Group A : images with distinctive objects. (have better segmentation results) • Group B : images without distinctive objects. • Currently the segmentation results are not satisfactory due to the limitation of the algorithm as well as the intrinsic difficulties of image segmentation on natural images. • Segmentation result is critical, we expect that query results of Group A would be better than Group B.

47. Region-based Image Retrieval

48. Conclusion • We established a framework for browsing and navigating geographic images • We use effective metadata representation and management for integration of multiple data sources and provide efficient access to the data sources. • We developed wavelet-based approach and keyblock-based approach to generalize the text-based IR techniques to geographic image retrieval. • Many remaining research issues.