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".

1. Lukas Tencer

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. 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. 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. Methods used

6. Demo

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. Results and discussion
 Average search time in database of 160 samples
partitioning grid 10*10 HOG with 9 bins 0.239 s

9. Thank you for your attention
lukas.tencer@gmail.com
http://tencer.hustej.net
@lukastencer
accuratelyrandom.blogspot.com
facebook.com/lukas.tencer

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