Special Topic on Image Retrieval Local Feature Matching Verification.

Special Topic on Image Retrieval

Local Feature Matching Verification

Geometric Verification

• Motivation– Remove false matches by checking geometric

consistency

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Red line: geometric consistent matchBlue line: geometric inconsistent match

Global Verification: RANSAC

• Take RANSAC as an example– Check geometric consistency from matched feature pairs.

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Local Geometric-Verification

• Locally nearest neighbors （ Video Goole, cvpr’03）– Matched regions should have a similar spatial layout.– For each match define its search area– Region in the search area that also matches casts a vote

for the image– Reject matches with no support

• Drawback– Sensitive to clutter

Hamming Embedding (ECCV’08)• Introduced as an extension of BOV [Jegou 08]

– Combination of– A partitioning technique (k-means)– A binary code that refine the descriptor

• Representation of a descriptor x– Vector-quantized to q(x) as in standard BOV– short binary vector b(x) for an additional localization in the Voronoi cell

• Two descriptors x and y match iif

Hamming Embedding

• Binary signature generation– Off-line learning

• Random matrix generation• Descriptor projection and assignment• Median values of projected descriptors

– On-line binarization• Quantization assignment• Descriptor projection• Computing the signature:

Local Geometric-Verification

• Bundled feature (CVPR’09)– Group local features in local MSER region.– Increase discriminative power of visual words.– Allowed to have large overlap error.

• Bundle comparison:– Mm(q; p): number of common visual words between two bundles

– Mg(q; p): inconsistency of geometric order in x- and y- direction.

• Drawbacks: Infeasible for rotated bundles.

– Visual words are bundled in MSER regions.– Spatial consistency for bundled features is utilized to weight visual

words. ( ; ) ( ; ) ( ; )m gM q p M q p M q p

Z. Wu, J. Sun, and Q. Ke, “Bundling Features for Large Scale Partial-Duplicate Web Image Search,” CVPR 09

),( pqMvv idftf

# of shared visual words

Spatial consistency

– Great performance for partial-dup detection in over 1 M database– Drawbacks: Infeasible for rotated bundles.

Local Geometric-VerificationBundled feature (CVPR’09)

Global Verification: RANSAC RANSAC: remove outliers by inlier classification

Inliers: true matched features Outliers: false matched features

Assumption of RANdom SAmple Consensus (RANSAC) The original data consists of inliers and outliers. A subset of inliers can estimate a model to optimally explain the inliers.

Estimate the affine transformation by RANSAC

Procedure: Iteratively select a random subset as hypothetical inliers 1. A model is fitted to the hypothetical inliers.2. All other data are tested against the fitted model for inlier classification.3. The model is re-estimated from all hypothetical inliers.4. The model is evaluated by estimating the error of the inliers relative to the model.

Drawbacks: Computationally expensive, not scalableFischler, et al., RANdom SAmple Consensus: a paradigm for model fitting with applications to image analysis and automated

cartography, Comm. of the ACM, 24:381-395, 1981

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Spatial Coding for Geometric Verification (ACM MM’ 10)

• Motivation– Encode local features’ relative positions into compact binary maps– Check spatial consistency of local matches for geometric verification

• Spatial coding maps– Relative spatial positions between local features.– Very efficient and high precision

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Ymap

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Zhou & Tian, Spatial Coding for large scale partial-duplicate image search. ACM Multimedia 2010.

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Spatial Map Generation

Rotate 45 degree counterclockwise

In previous case, each quadrant has one part Consider each quadrant is uniformly divided into two parts.

=

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Spatial Map Generation Generalized spatial map: GX and GY

Each quadrant is uniformly divided into r parts.

… …

r

k

2

k=r-1

k=1

k=0 X-map

X-map

X-map

Y-map

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Generalized Spatial Coding

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Spatial coding maps: Each quadrant uniformly divided into r parts. Decompose the division into r sub-division. Rotate each sub-division to align the axis.

New feature locations after rotation ：

Generalized spatial maps：

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Spatial Verification Verification with spatial maps GX and GY

Compare the spatial maps of matched features:

k=0, …, r-1; i, j=1, …, N; N: number of matched features Find and delete the most inconsistent matched pair,

recursively:

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Vx: inconsistent degree in X-map

Vy: inconsistent degree in Y-map

Identify i* and remove

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Geometric Verification with Coding Maps

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SUM

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y 2

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Image Plane Division (TOMCCAP’ 10)

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Geometric Square Coding

• Coordinate adjustment

• Square coding map

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Geometric Fan Coding

• Fan coding maps

• Coordinate adjustment

• Generalized coding maps

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Geometric Verification

• Compare the fan coding maps of matched features:

• Inconsistency measurement from geometric fan coding:

• Inconsistency measurement from geometric square coding:

• Inconsistency matrix: