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Survey of Algorithms to Query Image Databases COMP 290-72:Computational Geometry Benjamin Lok 11/2/98 Image from Kodak’s PhotoQuilt
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Outline of Talk w Overview of the problem w Three methods Color based Shape based Vision based w Conclusions Image from Microsoft Clip Gallery
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Problem w Query an image database w What does a “match” mean? Application dependent Notion of subjectivity Sensitivity to noise
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Problem w Semantic similarity is still not possible ex. “All images with cats” w To determine similarity, we need to a new: Metric Space Images from Microsoft Clip Gallery and website
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Ambiguity Guy Girl Guy Images from “Shrine to Long Haired Men” and “Videos of Women Getting Their Heads Shaved” websites
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Yossi Rubner, Leonidas Guibas, Carlos Tomasi (1997) Stanford Vision Laboratory The Earth Mover’s Distance, Multi-Dimensional Scaling, and Color-Based Image Retrieval Image from Microsoft Clip Gallery
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Color Signatures w Utilize the CIE-LAB color space Based on human perception of color w Map each pixel to a point in color space Common color values increase weight of point w Group clusters into points (8-12 per image) Rubner, Guibas, and Tomasi
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Earth Mover’s Distance w To compare two images, compute the “work” needed to move the cluster points from one image to the other Rubner, Guibas, and Tomasi
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Earth Mover’s Distance (cont) w Solving a linear programming problem: Given two signatures: p = {(p 1,w p1 ),…,(p m,w pm )} and q = {(q 1,w q1 ),…,(q n,w qn )} Find C where C ij is the amount of weight p i matched q j
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Applications w Visualize Databases (Queries and Results) w Scale the multiple dimensions into 2D using MDS and minimize STRESS Rubner, Guibas, and Tomasi
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Database Visualization Rubner, Guibas, and Tomasi
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Algorithm Recap w Map pixels to 3D color space points w Locate and compress “clusters” of points w 8 to 12 points determine the color signature w Calculate the Earth Mover’s Distance to determine “distance” between two images Image from YenPen Stationary Website
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Advantages w Based on human perception of color w Some invariance to small change in viewpoint and lighting w Meaningful metric w Relatively fast w Can embed multiple metrics Disadvantages w Depending on application, query format might be not be intuitive w Not much use for non- color images w False positives a real possibility depending on working domain
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Shaped-based Image Retrieval Using Geometric Hashing Scott D. Cohen and Leonidas J. Guibas 1997 Stanford Vision Laboratory Image from Microsoft Clip Gallery
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Overview w Implementation Search through 500 Chinese characters w Goals Provide invariance to scale, rotation, and translation speed and accuracy Cohen and Guibas
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Generating a Illustration w Illustration - set of curves that summarize an image Edgel detection Medial Axis determination Cohen and Guibas
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Approximating with Polylines w Convert medial axis representation to polylines w Tradeoff between speed and accuracy Cohen and Guibas
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Geometric Hashing w Geometric Hashing - method used to compare two point sets under some transformation group w Take each point and use it as the origin of a coordinate system Cohen and Guibas
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Geometric Hashing (cont) w If translating P by q j - p i produces a good match I i (P) and I j (Q) will match. w This property can be generalized to other transformation groups. w Each line segment is a basis of a coordinate system Translation, Rotation, and Orientation defined I(P) = transform all other segments into new CS Cohen and Guibas
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Notes on GH w Each segment will be transformed to 2m Coordinate systems w I(P) stores O(m 2 ) segments w Can be done as preprocessing step w Expressing the different possible transformations using each segment as a basis Cohen and Guibas
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Querying the Database w Query image undergoes the feature extraction process w For each query feature, a nearest-neighbor query is applied and the k closest or within some j w Similarity score increases if database image has a feature that is “close” to the query feature Cohen and Guibas
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“Closeness” w How do you describe the closeness of two lines? w Transform to a 4D space made of (l, ,a,b) w With two (l, ,a,b) descriptions for lines, can compute distance w Divide by standard deviation over sample of database features
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Details w Closeness is relative to database contents w Nearest-neighbor algorithm by Arya, Mount, et. al (1994). Query time for k nearest features is O(k log n) Cohen and Guibas
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Advantages w Fast Queries database of 500 characters in 1 second on SGI Indy w Queries based on important features Disadvantages w Working domain currently limited w Could get too expensive as complexity in images increases Cohen and Guibas
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A Multi-Resolution Technique for Comparing Images Using the Hausdorff Distance Daniel Huttenlocher and William Rucklidge 1992Cornell University Huttenlocher and Rucklidge
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Directed Hausdorff Distance w Given A={a 1, …, a m } and B={b 1, …, b m } w Identifies the point in A farthest from any point in B w Measures the degree of mismatch between between two sets.
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Properties of Hausdorff Distances w Not symmetric h(A,B) != h(B,A) w Compute k th maximum Notion of rank Reduces sensitivity Fraction of A within h(A,B) of B Obscured portions w h(A,B) = hypothesis h(B,A) = test
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Transformations t( ) = w Given A is an image, B is the model w Without Orientation, if A is in B then A undergoes transformation t. f B (t)=H(t(B),A) t=(t x,t y,s x,s y )forward f A (t)=H(A,t(B)) reverse
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Bidirectional Hausdorff Distance w Solve for which values of t, the following holds: w Results in searching a four dimensional space
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Restricting Search Space w Slope of f(t)=H LK (A,t(B)) is linear w Divide space into cells w Calculate H LK (A,t c (B)) w Determine a maximum delta per cell Based on limit in scale and translation Allows for quick rejection and acceptance w Label cells as interesting or disregard
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Restricting Search Space w Create smaller cells from interesting cells w Bounds based on transformations w Quickly narrow down to areas that could possibly be within of A
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Subtleties w Discretization useful if working in computer vision domain (integers) w Can compare partially obscured images w Optimizations Early rejection/acceptance w Pretty slow 200 to 250 seconds Website on submarines
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Huttenlocher and Rucklidge
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Advantages w Accurate w Geared towards image processing and vision w Partially obscured images w Searches similar to humans Disadvantages w Slow w No Orientation w Database must be specialized w Potential problems in generating queries
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Recap w Three Algorithms Color Based Color Signatures Earth Mover’s Distance Shaped Based Polylines Transform Invariant Sets Vision Based Hausdorff Distance Subdivision of Transformation Space www.sportsmanscaps.com
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Final Thoughts w Algorithms work well in various domains w Query construction not formalized w Other methods: wavelet-based texture-based object-based w Took 5 minutes to find “Shrine to Men with Long Hair” and “Videos of Women Getting Their Head Shaved” www.jerryspringer.com All other images generated by author using Paint Shop Pro
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