shape context descriptors are then indexed by a locality-sensitive hashing data structure aiming to perform approximate k-NN search in high dimensional spaces in sub-linear time.

**>>>Efficient Logo Retrieval Through Hashing Shape Context Descriptors.**

M. Rusiñol and J. Lladós. In *Proceedings of the Ninth IAPR International Workshop on Document Analysis Systems, DAS10*, pages 215-222, 2010. [pdf]

**CSC – color shape context**

For each sampled point pi, shape context histogram Si, To add color info to SC:

- A circular mask is defined as the region of interest centered at pi, the size of the mask is computed with relation to the
**mean distance**between all the points pairs in the shape. - The local color name histogram Ci
**combination of both descriptors Si and Ci at each point of the shape –****Distance between two point d(pi, pj)**:multiplying the distance of the shape context descriptor and the local color descriptor**Matching between sketch&image:**Given a set of local distances d(pi, pj) between all pairs of points, the

final distance between the sketch query and the image is determined by minimizing the total cost of matching

**bipartite graph matching** approach that puts in correspondence points having similar shape and color descriptions.

- Once all the n points in a shape are described by their shape context histogram, in order to match two shapes we have to find the point correspondences.
- The simplest way to compute the matching among the two set of points is by using a bipartite graph matching approach
- In order to obtain a more
**robust matching**, the most usual techniques involve the computation of**an affine transform**that matches the set of points from one shape to another.

**>>>>Perceptual Image Retrieval by Adding Color Information to the Shape Context Descriptor**M. Rusinol, F. Nourbakhsh, **D. Karatzas**, E. Valveny and J. Llados *Proceedings of the 20th International Conference on Pattern Recognition*, IEEE Press, pp. 1594-1597, Istanbul, Turkey, 2010 [pdf]

**Shapeme Histogram**

shapeme histogram descriptor was proposed by Mori et al. []

This description was inspired by the bag-of-words model. The main idea is to do vector quantization in the space of the shape context of all interest points.

- clustering stage of the shape context feature space:
**k-means algorithm** -
each shape context descriptor can be identified

by the index of the cluster which it belongs to

- Detection:
- extract n sampled point from edge map, then get the shape context hi
- then each shape context descriptor of the point is projected to the clustered space identified by single index Ii
- the query image can be represented by a histogram coding the frequency of appearance of each of k shapeme indices
- to find the matches, is to find the kNN in the spaece of shapeme histogram.