Example: Google Search by ImageGoogle Search by Image
Query Image Extract Features (Primitives) Image Database Features Database Similarity Measure Matched Results Relevance Feedback Algorithm From http://www.amrita.edu/cde/downloads/ACBIR.ppt Basic technique Each image in database is represented by a feature vector: x 1, x 2,...x N, where x i = (x i 1, x i 2, …, x i m ) Query is represented in terms of same features: Q =(Q 1, Q 2, …, Q m ) Goal: Find stored image with vector x i most similar to query vector Q
Distance measure: Possible distance measures d(Q, x i ): – Inner (dot) product – Histogram distance (for histogram features) – Graph matching (for shape features).
Some issues in designing a CBIR system Query format, ease of querying Speed Crawling, preprocessing Interactivity, user relevance feedback Visual features which to use? How to combine? Curse of dimensionality Indexing Evaluation of performance
Types of Features Typically Used Intensities Color Texture Shape Layout
Color auto-correlogram Pick any pixel p 1 of color C i in the image I. At distance k away from p 1 pick another pixel p 2. What is the probability that p 2 is also of color C i ? p1p1 p2p2 k Red ? Image: I From: http://www.cse.ucsc.edu/classes/ee264/Winter02/xgfeng.ppt
The auto-correlogram of image I for color C i, distance k: Integrates both color information and space information. From: http://www.cse.ucsc.edu/classes/ee264/Winter02/xgfeng.ppt
Two images with their autocorrelograms. Note that the change in spatial layout would be ignored by color histograms, but causes a significant difference in the autocorrelograms. From http://www.cs.cornell.edu/rdz/Papers/ecdl2/spatial.htm
Color histogram rank: 411; Auto-correlogram rank: 1 Color histogram rank: 310; Auto-correlogram rank: 5 Color histogram rank: 367; Auto-correlogram rank: 1 From: http://www.cs.cornell.ed u/rdz/Papers/ecdl2/spati al.htm
Texture Representations Each image has the same intensity distribution, but different textures Can use auto-correlogram based on intensity (gray-level co-occurrence)
Texture from entropy Images filtered by entropy: Each output pixel contains entropy value of 9x9 neighborhood around original pixel From: http://www.siim2011.org/abstracts/advanced_visualization_tools_ss_pao.html
Texture from fractal dimension From: http://www.cs.washington.edu/homes/rahul/data/ic cv07.pdf
Texture from Contrast Example : http://www.clear.rice.edu/elec301/Projects02/artSpy/grainin ess.html http://www.clear.rice.edu/elec301/Projects02/artSpy/grainin ess.html
Texture from Wavelets http://www.clear.rice.edu/elec301/Projects02/ artSpy/dwt.html
Shape representations Some of these need segmentation (another whole story!) Area, eccentricity, major axis orientation Skeletons, shock graphs Fourier transformation of boundary Histograms of edge orientations
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