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Multi-Local Feature Manifolds for Object Detection Oscar Danielsson (osda02@csc.kth.se) Stefan Carlsson (stefanc@csc.kth.se) Josephine Sullivan (sullivan@csc.kth.se) DICTA08

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The Problem Object categories are often modeled by collections (bag-of-features) or constellations (pictorial structures) of local features Many simple, shape-based objects don’t have any discriminative local appearance features ?

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The Multi-Local Feature A specific spatial constellation of oriented edgels (or other local content) Captures global shape properties “Weak” detector of shape-based object categories Described by coordinate vector: (x 1,…,x 12 )

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Modeling Intra-Class Variation

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1. Generate coordinate vectors by clicking corresponding edgels in a (small) number of training images 2. Align coordinate vectors wrt. similarity transform

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Modeling Intra-Class Variation 3. Extend coordinate vectors into their convex hull

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Detection

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For each occurrence x 1 of c 1 For each consistent occurrence x 2 of c 2 Sample from p(x 4,x 3 |x 2,x 1 ) to hypothesize image locations of c 3 and c 4 Sample image edgels Compute normalized distance to convex hull of training features If distance is below threshold, an instance was detected End For

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Detection For each occurrence x 1 of c 1 For each consistent occurrence x 2 of c 2 Sample from p(x 4,x 3 |x 2,x 1 ) to hypothesize image locations of c 3 and c 4 Sample image edgels Compute normalized distance to convex hull of training features If distance is below threshold, an instance was detected End For

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Detection For each occurrence x 1 of c 1 For each consistent occurrence x 2 of c 2 Sample from p(x 4,x 3 |x 2,x 1 ) to hypothesize image locations of c 3 and c 4 Sample image edgels Compute normalized distance to convex hull of training features If distance is below threshold, an instance was detected End For

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Detection For each occurrence x 1 of c 1 For each consistent occurrence x 2 of c 2 Sample from p(x 4,x 3 |x 2,x 1 ) to hypothesize image locations of c 3 and c 4 Sample image edgels Compute normalized distance to convex hull of training features If distance is below threshold, an instance was detected End For

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Detection For each occurrence x 1 of c 1 For each consistent occurrence x 2 of c 2 Sample from p(x 4,x 3 |x 2,x 1 ) to hypothesize image locations of c 3 and c 4 Sample image edgels Compute normalized distance to convex hull of training features If distance is below threshold, an instance was detected End For

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Detection For each occurrence x 1 of c 1 For each consistent occurrence x 2 of c 2 Sample from p(x 4,x 3 |x 2,x 1 ) to hypothesize image locations of c 3 and c 4 Sample image edgels Compute normalized distance to convex hull of training features If distance is below threshold, an instance was detected End For

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Detection For each occurrence x 1 of c 1 For each consistent occurrence x 2 of c 2 Sample from p(x 4,x 3 |x 2,x 1 ) to hypothesize image locations of c 3 and c 4 Sample image edgels Compute normalized distance to convex hull of training features If distance is below threshold, an instance was detected End For

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Detection For each occurrence x 1 of c 1 For each consistent occurrence x 2 of c 2 Sample from p(x 4,x 3 |x 2,x 1 ) to hypothesize image locations of c 3 and c 4 Sample image edgels Compute normalized distance to convex hull of training features If distance is below threshold, an instance was detected End For

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Experiments Detection performance was evaluated on a standard database (ETHZ Shape Classes) and we want to investigate: Is a multi-local feature a good weak detector? How many local features should be used?

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Mugs - Training 3 1 8 10 149 7 1213 2 6 11 5 4 3 1 8 10 14 9 7 12 13 2 6 11 5 4 25 training images were downloaded from Google images 14 edgels constituting a multilocal feature were marked in each training image

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Mugs - Results

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Performance decreases when adding more than 9 local features 0.4 60.6 %

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Bottles - Training 12 1 10 7 11 9 8 6 2 5 3 4 1 10 7 11 9 8 6 2 5 3 4 12 25 training images were downloaded from Google images 12 edgels constituting a multilocal feature were marked in each training image

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Bottles - Results

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0.4 72.7 %

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Apple logos - Training 20 training images were downloaded from Google images 12 edgels constituting a multilocal feature were marked in each training image

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Apple logos - Results

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Performance decreases when adding more than 11 local features 0.4 77.3 %

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Conclusions A multi-local feature is a good weak detector of shape-based object categories The best performance is achieved with multi- local features with a moderate number of local features Convex combinations of valid exemplars are in general also valid exemplars (we can extend a few training examples into their convex hull)

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Future Work Automatic learning of multi-local features Building combinations of multi-local feature detectors into an object detection system

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Related Work Pictorial Structures E.g.. Felzenszwalb, Huttenlocher. Pictorial Structures for Object Recognition, IJCV No. 1, January 2005. Constellation Models E.g.. Fergus, Perona, Zisserman. Object class recognition by unsupervised scale-invariant learning, CVPR03. Differences Different detection methods Use rich local features

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Thanks!

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Representation The multi-local feature manifold consists of all convex combinations of the training examples

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