Attributes and Categories for Generic Instance Search From One Example

Ran Tao, Arnold W.M. Smeulders, Shih-Fu Chang; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 177-186

Abstract


This paper aims for generic instance search from one example where the instance can be an arbitrary 3D object like shoes, not just near-planar and one-sided instances like buildings and logos. Firstly, we evaluate state-of-the-art instance search methods on this problem. We observe that what works for buildings loses its generality on shoes. Secondly, we propose to use automatically learned category-specific attributes to address the large appearance variations present in generic instance search. On the problem of searching among instances from the same category as the query, the category-specific attributes outperform existing approaches by a large margin. On a shoe dataset containing 6624 shoe images recorded from all viewing angles, we improve the performance from 36.73 to 56.56 using category-specific attributes. Thirdly, we extend our methods to search objects without restricting to the specifically known category. We show the combination of category-level information and the category-specific attributes is superior to combining category-level information with low-level features such as Fisher vector.

Related Material


[pdf]
[bibtex]
@InProceedings{Tao_2015_CVPR,
author = {Tao, Ran and Smeulders, Arnold W.M. and Chang, Shih-Fu},
title = {Attributes and Categories for Generic Instance Search From One Example},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}