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Scalable Image Matching

Scalable Image Matching. David Strickland ENGN 256 Spring 2013. Reference Paper: Inverted Index Compression for Scalable Image Matching.

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Scalable Image Matching

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  1. Scalable Image Matching David Strickland ENGN 256 Spring 2013

  2. Reference Paper: Inverted Index Compression for Scalable Image Matching • Chen, D.M.; Tsai, S.S.; Chandrasekhar, V.; Takacs, G.; Vedantham, R.; Grzeszczuk, R.; Girod, B., "Inverted Index Compression for Scalable Image Matching," Data Compression Conference (DCC), 2010 , vol., no., pp.525,525, 24-26 March 2010

  3. Scalable Image Matching for the BlindFind Project • BlindFind aims to help the blind navigate unfamiliar indoor environments to locate places via a wearable navigation device powered by crowd-sourced maps • The device needs to know its map location • Image matching is required • Image matching must be fast and real time • Needs to be scalable

  4. Image Matching • Most solutionsare base on local image features: • SIFT (Scale-Invariant Feature Transform) • SURF (Speeded Up Robust Features” • CHoG (Compressed Histogram of Gradients) • Process: • Detect Features • Extract Feature Locations and Descriptors • Compare the Features of two images to determine similarity

  5. Example of Features http://en.wikipedia.org/wiki/File:Sift_keypoints_filtering.jpg

  6. Problem • Comparing an image with every image in a large database takes an extremely long amount of time • Doesn’t scale • Some databases contain millions of images

  7. Solution: Vocabulary Tree + Inverted Index • Detect Features • Extract Feature Locations and Descriptors • Quantize Descriptors into a Vocabulary Tree • Score Database Images using Inverted Index • Pairwise Match using Geometric Check

  8. Vocabulary Tree + Inverted Index • The Vocabulary Tree is a tree-structured vector quantizer constructed by hierarchical k-means clustering of feature descriptors. • Inverted Index: Each node has two lists • Image IDs • Array of counts 1Image from Chen et al.

  9. Similarity Scoring & Memory Usage • Each image ik1 in the database of N images is given a similarity score • For each node visited by query descriptors the node’s inverted list of images all have the scores incremented : Where:

  10. New Problem • Inverted index requires lots of memory • Memory usage for VT with K leaf nodes, where Nk database images have visited each node: • Need to find a way to reduce memory usage of the inverted index

  11. New Solutions for Dealing with Large Amounts of Data • Fast decoding methods • Carryover Code (32 bit word) • Recursive Bottom Up Complete (RBUC) • Inverted Index Compression • Encode Image IDs by consecutive differences • Reorder database to minimize differences • Soft-binned feature descriptor histograms • Improves accuracy of VT search

  12. Fast decoding methods • Carryover Code (32 bit word) • 2-bit selector + 30 bit data portion • Selector indicates the precision of the data portion • E.g. 30 1 bit data symbols, 15 2 bit data symbols, etc. • Recursive Bottom Up Complete (RBUC) • Similar to carryover code encoding • The precision for each pair is calculated as the max of the two precisions • Then P’ itself is encoded

  13. Inverted Index Compression • Encode each inverted index’s Image IDs by consecutive differences • Inverted index compression techniques can significantly reduce memory usage by up to 5X without any loss in recognition accuracy • Reorder database to minimize differences • Minimize:

  14. Soft-binned feature descriptor histograms • Classify a feature descriptor to k nearest tree nodes instead of just nearest tree node • Soft-binned tree gives improvement in classification accuracy • Disadvantage: • Each database feature now appears in k different inverted lists • Inverted Index is k times as large

  15. Schedule I: VT/II Implementation • Week 1: Research Vocabulary Tree / Inverted Index, Determine which libraries to use • Week 2: Implement Feature Locator/Descriptors • Week 3: Implement Quantization of Descriptors in VT • Week 4: Implement Database scoring scheme using Inverted Index • Week 5: Milestone: Mid Project Presentation, Combine Previous parts, Pairwise Match to retrieve a single image

  16. Schedule II: Compression • Week 6: Inverted Index Image ID storage • Week 7: Fast Decoding • Week 8: Soft Binned Tree, Analysis • Week 9: Final Project Presentation

  17. References • David M. Chen, Sam S. Tsai, Vijay Chandrasekhar, Gabriel Takacs, Ramakrishna Vedantham, Radek Grzeszczuk, Bernd Girod, “Inverted Index Compression for Scalable Image Matching”

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