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Lukas Tencer
Our objectives Input:    Album of images, small db vs. large db    Binary user sketch Output: Images similar to the sk...
Existing solutions Discrete: Oriented Chamfer Matching + edgel p(x,y, α)    Index: inverted index list GF-HOG    Grad...
Our approach Descriptor:    HOG + DDT captures orientation information + distance        1. Sobel edge detector       ...
Methods used
Demo
Contribution Descriptor HOG+DDT    Improved accuracy of retrieval    Robustness against affine transformations Adaptiv...
Results and discussion Average search time in database of 160 samples partitioning grid 10*10 HOG with 9 bins 0.239 s
Thank you for your attention                lukas.tencer@gmail.com                http://tencer.hustej.net                ...
Sources http://www.mathworks.com http://homes.ieu.edu.tr/~hakcan/projects/kdtree http://visual.ipan.sztaki.hu/mysquash/...
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Web-based framework for online sketch-based image retrieval

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My presentation for course SYS821 "Pattern recognition and inspection" at ETS. This describes implementation of my project on topic "Web-based framework for online sketch-based image retrieval".

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Web-based framework for online sketch-based image retrieval

  1. 1. Lukas Tencer
  2. 2. Our objectives Input:  Album of images, small db vs. large db  Binary user sketch Output: Images similar to the sketch- Accurate- Fast- Scalable- Online
  3. 3. Existing solutions Discrete: Oriented Chamfer Matching + edgel p(x,y, α)  Index: inverted index list GF-HOG  Gradient field of Gradient of Histograms – scales badly HOG + Structure Tensor  Good abstraction over data representation Continuous:  HMM probability fitting  Energy-based deformation
  4. 4. Our approach Descriptor:  HOG + DDT captures orientation information + distance  1. Sobel edge detector  2. Spatial partitioning  3. calculated gradients and distance transformation  4. Get histogram  F(x) = (f(x1), f(x2)…f(xn)) Search space:  subspace for neighborhood (n/5)  k-d tree ( search in log n)  k-means  k-NN search O (k N^(1-1/k)) Online search  Partial results  R(x) = R(x1)+R(x2)…R(xn)
  5. 5. Methods used
  6. 6. Demo
  7. 7. Contribution Descriptor HOG+DDT  Improved accuracy of retrieval  Robustness against affine transformations Adaptive neighborhood search  Even higher robustness against translation and rotation Online search approach  Faster retrieval rate  Less user input required
  8. 8. Results and discussion Average search time in database of 160 samples partitioning grid 10*10 HOG with 9 bins 0.239 s
  9. 9. Thank you for your attention lukas.tencer@gmail.com http://tencer.hustej.net @lukastencer accuratelyrandom.blogspot.com facebook.com/lukas.tencer
  10. 10. Sources http://www.mathworks.com http://homes.ieu.edu.tr/~hakcan/projects/kdtree http://visual.ipan.sztaki.hu/mysquash/node5.html http://www.eecs.umich.edu/~silvio/teaching/lectures/ phog.html

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