OTC: A Novel Local Descriptor for

Scene Classification

Ran Margolin, Lihi Zelnik-Manor and Ayellet Tal
Technion Haifa, Israel



Scene classification is the task of determining the scene type in which a photograph was taken. In this paper we present a novel local descriptor suited for such a task: Oriented Texture Curves (OTC). Our descriptor captures the texture of a patch along multiple orientations, while maintaining robustness to illumination changes, geometric distortions and local contrast differences. We show that our descriptor outperforms all state-of-the-art descriptors for scene classification algorithms on the most extensive scene classification benchmark to-date.


Ran Margolin, Lihi Zelnik-Manor and Ayellet Tal: “OTC: A Novel Local Descriptor for Scene Classifcation”, ECCV 2014 [pdf]





[1] Lazebnik et al. CVPR'06 [4] Xiao et al. CVPR'10 [7] Su et al. IJCV'12
[2] Shechtman et al. CVPR'07 [5] Felzenszwalb etl al. PAMI'10 [8] Donahue et al. ICML'14
[3] Shen et al. CVPR'13 [6] Kwitt et al. ECCV'12 [9] Gong et al. CoRR'14



OTC descriptor for a single image [download] - Tested on Windows 64-bit Matlab 2012b & 2013a.
The code contains the Matlab function im2colstep.m, written by Dr. Ron Rubinstein.

The code is for academic purposes only. Please cite this paper if you make use of it:

@conference{margoinOTC, title={OTC: A Novel Local Descriptor for Scene Classification}, author={Margolin, R. and Zelnik-Manor, L. and Tal, A}, year = {2014}, booktitle = {ECCV}}

In case of any problems please contact us at: margolin (at) tx.technion.ac.il or hovav (at) ee.technion.ac.il