Exploring Residual and Spatial Consistency for Object Detection

Hao Wang, Ya Zhang, Zhe Xu

Abstract

Local image features show a high degree of repeatability, while their local appearance usually does not bring enough discriminative pattern to obtain a reliable matching. In this paper, we present a new object matching algorithm based on a novel robust estimation of residual consensus and flexible spatial consistency filter. We evaluate the similarity between different homography model via two-parameter integrated Weibull distribution and inlier probabilities estimates, which can select uncontaminated model to help eliminating outliers. Spatial consistency test was encoded by the geometric relationships of domain knowledge in two directions, which is invariant to scale, rotation, and translation especially robust to the flipped image. Experiment results on nature images with clutter background demonstrate our method effectiveness and robustness.

References

  1. Bagdanov, A. D., Ballan, L., Bertini, M., and Del Bimbo, A. (2007). Trademark matching and retrieval in sports video databases. In Proceedings of the international workshop on Workshop on multimedia information retrieval, pages 79-86. ACM.
  2. Chin, T.-J., Yu, J., and Suter, D. (2010). Accelerated hypothesis generation for multi-structure robust fitting. In Computer Vision-ECCV 2010, pages 533- 546. Springer.
  3. Chum, O. and Matas, J. (2005). Matching with prosacprogressive sample consensus. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, volume 1, pages 220-226. IEEE.
  4. Chum, O., Matas, J., and Kittler, J. (2003). Locally optimized ransac. In Pattern Recognition, pages 236-243. Springer.
  5. Comaniciu, D. and Meer, P. (2002). Mean shift: A robust approach toward feature space analysis. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 24(5):603-619.
  6. Jegou, H., Douze, M., and Schmid, C. (2008). Hamming embedding and weak geometric consistency for large scale image search. In Computer Vision-ECCV 2008, pages 304-317. Springer.
  7. Jiang, Y., Meng, J., and Yuan, J. (2011). Grid-based local feature bundling for efficient object search and localization. In Image Processing (ICIP), 2011 18th IEEE International Conference on, pages 113-116. IEEE.
  8. Kalantidis, Y., Pueyo, L. G., Trevisiol, M., van Zwol, R., and Avrithis, Y. (2011). Scalable triangulation-based logo recognition. In Proceedings of the 1st ACM International Conference on Multimedia Retrieval, page 20. ACM.
  9. Lazebnik, S., Schmid, C., and Ponce, J. (2006). Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, volume 2, pages 2169-2178. IEEE.
  10. Li, T., Mei, T., Kweon, I.-S., and Hua, X.-S. (2011). Contextual bag-of-words for visual categorization. Circuits and Systems for Video Technology, IEEE Transactions on, 21(4):381-392.
  11. Lowe, D. G. (2004). Distinctive image features from scaleinvariant keypoints. International Journal of Computer Vision, 60(2):91-110.
  12. Ni, K., Jin, H., and Dellaert, F. (2009). Groupsac: Efficient consensus in the presence of groupings. In Computer Vision, 2009 IEEE 12th International Conference on, pages 2193-2200. IEEE.
  13. Raguram, R., Frahm, J.-M., and Pollefeys, M. (2008). A comparative analysis of ransac techniques leading to adaptive real-time random sample consensus. In Computer Vision-ECCV 2008, pages 500-513. Springer.
  14. Russell, B. C., Freeman, W. T., Efros, A. A., Sivic, J., and Zisserman, A. (2006). Using multiple segmentations to discover objects and their extent in image collections. In Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, volume 2, pages 1605-1614. IEEE.
  15. Sattler, T., Leibe, B., and Kobbelt, L. (2009). Scramsac: Improving ransac's efficiency with a spatial consistency filter. In Computer Vision, 2009 IEEE 12th International Conference on, pages 2090-2097. IEEE.
  16. Sivic, J. and Zisserman, A. (2003). Video google: A text retrieval approach to object matching in videos. In Computer Vision, 2003. Proceedings. Ninth IEEE International Conference on, pages 1470-1477. Ieee.
  17. Sivic, J. and Zisserman, A. (2009). Efficient visual search of videos cast as text retrieval. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 31(4):591- 606.
  18. Tordoff, B. J. and Murray, D. W. (2005). Guided-mlesac: Faster image transform estimation by using matching priors. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 27(10):1523-1535.
  19. Torr, P. H. and Zisserman, A. (2000). Mlesac: A new robust estimator with application to estimating image geometry. Computer Vision and Image Understanding, 78(1):138-156.
  20. Wu, Z., Ke, Q., Isard, M., and Sun, J. (2009). Bundling features for large scale partial-duplicate web image search. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pages 25-32. IEEE.
  21. Zhang, Y., Jia, Z., and Chen, T. (2011). Image retrieval with geometry-preserving visual phrases. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pages 809-816. IEEE.
Download


Paper Citation


in Harvard Style

Wang H., Zhang Y. and Xu Z. (2014). Exploring Residual and Spatial Consistency for Object Detection . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-004-8, pages 191-197. DOI: 10.5220/0004746801910197


in Bibtex Style

@conference{visapp14,
author={Hao Wang and Ya Zhang and Zhe Xu},
title={Exploring Residual and Spatial Consistency for Object Detection},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={191-197},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004746801910197},
isbn={978-989-758-004-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014)
TI - Exploring Residual and Spatial Consistency for Object Detection
SN - 978-989-758-004-8
AU - Wang H.
AU - Zhang Y.
AU - Xu Z.
PY - 2014
SP - 191
EP - 197
DO - 10.5220/0004746801910197