Structural Kernel Learning for Large Scale Multiclass Object Co-Detection

Zeeshan Hayder, Xuming He, Mathieu Salzmann; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015, pp. 2632-2640

Abstract


Exploiting contextual relationships across images has recently proven key to improve object detection. The resulting object co-detection algorithms, however, fail to exploit the correlations between multiple classes and, for scalability reasons are limited to modeling object instance similarity with relatively low-dimensional hand-crafted features. Here, we address the problem of multiclass object co-detection for large scale datasets. To this end, we formulate co-detection as the joint multiclass labeling of object candidates obtained in a class-independent manner. To exploit the correlations between objects, we build a fully-connected CRF on the candidates, which explicitly incorporates both geometric layout relations across object classes and similarity relations across multiple images. We then introduce a structural boosting algorithm that lets us exploits rich, high-dimensional deep network features to learn object similarity within our fully-connected CRF. Our experiments on PASCAL VOC 2007 and 2012 evidences the benefits of our approach over object detection with RCNN, single-image CRF methods and state-of-the-art co-detection algorithms.

Related Material


[pdf]
[bibtex]
@InProceedings{Hayder_2015_ICCV,
author = {Hayder, Zeeshan and He, Xuming and Salzmann, Mathieu},
title = {Structural Kernel Learning for Large Scale Multiclass Object Co-Detection},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2015}
}