SWE 423: Multimedia Systems

1. SWE 423: Multimedia Systems Multimedia Databases

2. Outline • Image Processing Basics • Image Features • Image Segmentation • Textbook Section 4.3 • Additional Reference: Wasfi Al-Khatib, Y. Francis Day, Arif Ghafoor, and P. Bruce Berra. Semantic modeling and knowledge representation in multimedia databases. IEEE Transactions on Knowledge and Data Engineering, 11(1):64-80, 1999.

3. Image Processing • Image processing involves the analysis of scenes or the reconstruction of models from images representing 2D or 3D objects. • Image Analysis • Identifying Image Properties (Image Features) • Image Segmentation • Image Recognition • We will look at image processing from a database perspective. • Objective: Design of robust image processing and recognition techniques to support semantic modeling, knowledge representation, and querying of images.

4. Semantic Modeling and Knowledge Representation in Image Databases • Feature Extraction. • Salient Object Identification. • Content-Based Indexing and Retrieval. • Query Formulation and Processing.

5. Semantic Specification Knowledge Base Semantic Identification Process Object Recognition Process Object Models Feature Extraction Process Feature Specification Image Data Still Video Frames Multi-Level Abstraction Semantic Modeling And Knowledge Representation Layer Object Recognition Layer Feature Extraction Layer Multimedia Data

6. Feature Extraction Layer • Image features: Colors, Textures, Shapes, Edges, ...etc. • Features are mapped into a multi-dimensional feature space allowing similarity-based retrieval. • Features can be classified into two types: Global and Local.

7. Global Features • Generally emphasize coarse-grained pattern matching techniques. • Transform the whole image into a functional representation. • Finer details within individual parts of the image are ignored. • Examples: Color histograms and coherence vectors, Texture, Fast Fourier Transform, Hough Transform, and Eigenvalues. • What are some of the example queries?

8. Color Histogram • How many pixels of the image take a specific color • In order to control the number of colors, the domain is discretized • E.g. consider the value of the two leftmost bits in each color channel (RGB). • In this case , the number of different colors is equal to __________ • How can we determine whether two images are similar using the color histogram?

9. Color Coherence Vector • Based on the color histogram • Each pixel is checked as to whether it is within a sufficiently large one-color environment or not. • i.e. in a region related by a path of pixels of the same color • If so, the pixel is called coherent, otherwise incoherent • For each color j, compute the number of coherent and incoherent pixels (j , j), j = 1, ..., J • When comparing two images with color coherence vectors (j , j) and (j , j), j = 1, ..., J, we may use the expression

10. Texture • Texture is a small surface structure • Natural or artificial • Regular or irregular • Examples include • Wood barks • Knitting patterns • The surface of a sponge

11. Texture Examples • Artificial/periodic • Artificial/non-periodic • Photographic/pseudo-periodic • Photographic/random • Photographic/structured • Inhomogeneous (non-texture)

12. Texture • Two basic approaches to study texture • Structural analysis searches for small basic components and an arrangement rule • Statistical analysis describes the texture as a whole based on specific attributes (local gray-level variance, regularity, coarseness, orientation, and contrast. • Either done in the spatial domain or the spatial frequency domain

13. Global Features • Advantages: • Simple. • Low computational complexity. • Disadvantages: • Low accuracy

14. Local Features • Images are segmented into a collection of smaller regions, with each region representing a potential object of interest (fine-grained). • An object of interest may represent a simple semantic object (e.g. a round object). • Choice of features is domain specific: • X-ray imaging, GIS, ...etc require spatial features (e.g. shapes [may be calculated through edges] and dimensions.) • Paintings, MMR imaging, ...etc may use color features in specific regions of the image.

15. Edge Detection • A given input image E is used to gradually compute a (zero-initialized) output image A. • A convolution mask runs across E pixel by pixel and links the entries in the mask at each position that M occupies in E with the gray value of the underlying image dots. • The result of the linkage (and the subsequent sum across all products from the mask entry and the gray value of the underlying image pixel) is written to the output image A.

16. Convolution • Convolution is a simple mathematical operation which is fundamental to many common image processing operators. • Convolution provides a way of `multiplying together' two arrays of numbers, generally of different sizes, but of the same dimensionality, to produce a third array of numbers of the same dimensionality. • This can be used in image processing to implement operators whose output pixel values are simple linear combinations of certain input pixel values. • The convolution is performed by sliding the kernel over the image, generally starting at the top left corner, so as to move the kernel through all the positions where the kernel fits entirely within the boundaries of the image.

17. Convolution Computation • If the image E has M rows and N columns, and the kernel K has m rows and n columns, then the size of the output image A will have M - m + 1 rows, and N - n + 1 columns and is given by: • Example page 60. • http://homepages.inf.ed.ac.uk/rbf/HIPR2/sobel.htm

18. Similarity Metrics • Minkowski Distance • Weighted Distance • Average Distance • Color Histogram Intersection

19. Prototype Systems • QBIC (http://www.hermitagemuseum.org) • Uses color, shape, and texture features • Allows queries by sketching features and providing color information • Chabot (Cypress) • Uses color and textual annotation. • Improved performance due to textual annotation (Concept Query) • KMeD • Uses shapes and contours as features. • Features are extracted automatically in some cases and manually in other cases.

20. Demo (Andrew Berman & Linda G. Shapiro ) • http://www.cs.washington.edu/research/imagedatabase/demo/seg/ • http://www.cs.washington.edu/research/imagedatabase/demo/edge/ • http://www.cs.washington.edu/research/imagedatabase/demo/fids/

21. Image Segmentation • Assigning a unique number to “object” pixels based on different intensities or colors in the foreground and the background regions of an image • Can be used in the object recognition process, but it is not object recognition on its own • Segmentation Methods • Pixel oriented methods • Edge oriented methods • Region oriented methods • ....

22. Pixel-Oriented Segmentation • Gray-values of pixels are studied in isolation • Looks at the gray-level histogram of an image and finds one or more thresholds in the histogram • Ideally, the histogram has a region without pixels, which is set as the threshold, and hence the image is divided into a foreground and a background based on that (Bimodal Distribution) • Major drawback of this approach is that object and background histograms overlap. • Bimodal distribution rarely occurs in nature.

23. Edge-Oriented Segmentation • Segmentation is carried out as follows • Edges of an image are extracted (using Canny operators, e.g.) • Edges are connected to form closed contours around the objects. • Hough Transform • Usually very expensive • Works well with regular curves (application in manufactured parts) • May work in presence of noise

24. Region-Oriented Segmentation • A major disadvantage of the previous approaches is the lack of “spatial” relationship considerations of pixels. • Neighboring pixels normally have similar properties • The segmentation (region-growing) is carried out as follows • Start with a “seed” pixel. • Pixel’s neighbors are included if they have some similarity to the seed pixel, otherwise they are not. • Homogeneity condition • Uses an eight-neighborhood (8-nbd) model

25. Region-Oriented Segmentation • Homogeneity criterion: Gray-level mean value of a region is usually used • With standard deviation • Drawbacks: Computationally expensive.

26. Water Inflow Segmentation • Fill a gray-level image gradually with water. • Gray-levels of pixels are taken as height. • The higher the water rises, the more pixels are flooded • Hence, you have lands and waters • Lands correspond to “objects”

27. Object Recognition Layer • Features are analyzed to recognize objects and faces in an image database. • Features are matched with object models stored in a knowledge base. • Each template is inspected to find the closest match. • Exact matches are usually impossible and generally computationally expensive. • Occlusion of objects and the existence of spurious features in the image can further diminish the success of matching strategies.

28. Template Matching Techniques • Fixed Template Matching • Useful if object shapes do not change with respect to the viewing angle of the camera. •  Deformable Template Matching • More suitable for cases where objects in the database may vary due to rigid and non-rigid deformations.

29. Fixed Template Matching • Image Subtraction: • Difference in intensity levels between the image and the template is used in object recognition. • Performs well in restricted environments where imaging conditions (such as image intensity) between the image and the template are the same.  • Matching by correlation: • utilizes the position of the normalized cross-correlation peak between a template and image. • Generally immune to noise and illumination effects in the image. • Suffers from high computational complexity caused by summations over the entire template.

30. Deformable Template Matching • Template is represented as a bitmap describing the characteristic contour/edges of an object shape. • An objective function with transformation parameters which alter the shape of the template is formulated reflecting the cost of such transformations. • The objective function is minimized by iteratively updating the transformations parameters to best match the object. • Applications include: handwritten character recognition and motion detection of objects in video frames. 

31. Prototype System: KMeD • Medical objects belonging only to patients in a small age group are identified automatically in KMeD. • Such objects have high contrast with respect to their background and have relatively simple shapes, large sizes, and little or no overlap with other objects. • KMeD resorts to a human-assisted object recognition process otherwise.

32. Demo • http://www.cs.washington.edu/research/imagedatabase/demo/cars/ (check car214)

33. Spatial Modeling and Knowledge Representation Layer (1) • Maintain the domain knowledge for representing spatial semantics associated with image databases. • At this level, queries are generally descriptive in nature, and focus mostly on semantics and concepts present in image databases. • Semantics at this level are based on ``spatial events'' describing the relative locations of multiple objects. • An example involving such semantics is a range query which involves spatial concepts such as close by, in the vicinity, larger than. (e.g. retrieve all images that contain a large tumor in the brain).

34. Spatial Modeling and Knowledge Representation Layer (2) • Identify spatial relationships among objects, once they are recognized and marked by the lower layer using bounding boxes or volumes. • Several techniques have been proposed to formally represent spatial knowledge at this layer. • Semantic networks • Mathematical logic • Constraints • Inclusion hierarchies • Frames.

35. Semantic Networks • First introduced to represent the meanings of English sentences in terms of words and relationships between them. • Semantic networks are graphs of nodes representing concepts that are linked together by arcs representing relationships between these concepts. • Efficiency in semantic networks is gained by representing each concept or object once and using pointers for cross references rather than naming an object explicitly every time it is involved in a relation. • Example: Type Abstraction Hierarchies (KMeD)

36. Brain Lesions Representation

37. TAH Example

38. Constraints-based Methodology • Domain knowledge is represented using a set of constraints in conjunction with formal expressions such as predicate calculus or graphs. • A constraint is a relationship between two or more objects that needs to be satisfied.

39. Example: PICTION system • Its architecture consists of a natural language processing module (NLP), an image understanding module (IU), and a control module. • A set of constraints is derived by the NLP module from the picture captions. These constraints (called Visual Semantics by the author) are used with the faces recognized in the picture by the IU module to identify the spatial relationships among people. • The control module maintains the constraints generated by the NLP module and acts as a knowledge-base for the IU module to perform face recognition functions.

41. Mathematical Logic • Iconic Indexing by 2D strings: Uses projections of salient objects in a coordinated system. • These projections are expressed in the form of 2D strings to form a partial ordering of object projections in 2D. • For query processing, 2D subsequence matching is performed to allow similarity-based retrieval. • Binary Spatial Relations: Uses Allen's 13 temporal relations to represent spatial relationships.

42. Inclusion Hierarchies • The approach is object-oriented and uses concept classes and attributes to represent domain knowledge. • These concepts may represent image features, high-level semantics, semantic operators and conditions.

43. Frames • A frame usually consists of a name and a list of attribute-value pairs. • A frame can be associated with a class of objects or with a class of concepts. • Frame abstractions allow encapsulation of file names, features, and relevant attributes of image objects.