Abstract

With the SIFT and SURF based recognition, the paper presents the impact of salient features in object recognition. We use the two well-known image descriptors in the bag of words framework on five online available standard datasets. Experiments show that by introducing saliency in the bag of words model, state-of-the-art performance can still be retained while reducing considerable amount of data processing and thus achieving faster execution times.

References

• C. Schmid and R. Mohr. "Local greyvalue invariants for image retrieval. " TPAMI, 1997.
• D. Lowe. ," Object recognition from local scale-invariant features",ICCV1999.
• Herbert Bay , Tinne Tuytelaars, Luc Van Gool, "SURF: Speeded Up Robuts Features", ECCV. 2006 .
• Csurka, G. , Dance, C. , Fan, L. , Williamowski, J. , Bray, C. : Visual categorization with bags of keypoints. In: ECCV'04 workshop on Statistical Learning in Computer Vision. (2004) 59–74
• Lowe, D. "Distinctive image features from scale-invariant keypoints. " IJCV 60 (2004)
• Fergus, R. , Fei-Fei, L. , Perona, P. , Zisserman, A. : Learning object categories from google's image search. In: ICCV. (2005) II: 1816–1823
• Leung, T. , Malik, J. "Representing and recognizing the visual appearance of materials using three-dimensional textons. " IJCV 43 (2001) 29–44
• A. Rabinovich, A. Vedaldi, C. Galleguillos, E. Wiewiora, S. Belongie, "Objects in context", ICCV, 2007.
• A. Torralba, "Contextual priming for object detection", International Journal of Computer Vision (IJCV) 53 (2) (2003) 153–167
• J. Verbeek, B. Triggs, Scene segmentation with CRFs learned from partially labeled images, in: NIPS, vol. 11, 2008.
• L. Wolf, S. Bileschi, "A critical view of context", International Journal of Computer Vision (2006).
• P. Duygulu, K. Barnard, J. F. G. de Freitas and D. A. Forsyth, "Object recognition as Machine translation: Learning a Lexicon for a fixed image vocabulary" Computer Vision ECCV2002.
• Hall, D. , Leibe, B. , Schiele, B. : "Saliency of interest points under scale changes". In: BMVC. (2002)
• Tie Liu, "Learning to detect a salient object" Pattern Analysis and Machine Intelligence, IEEE Transactions on Feb. 2011
• Timor Kadir and Michael Brady "Saliency, scale and image description" Jurnal of computer vision (2001)
• P. J. Flynn. "Saliencies and symmetries: Toward 3d object recognition from large model databases". In CVPR'92, pages 322–327, 1992
• B. Schiele and J. L. Crowley. "Probabilistic object recognition using multidimensional receptive ?eld histograms". In ICPR96, Vienna, Austria, 1996.
• N. Sebe and M. S. Lew. "Salient points for content-based retrieval. " In BMVC'01, pages 401–410, 2001.
• K. N. Walker, T. F. Cootes, and Chris Taylor. "Locating salient object features". In BMVC'98,pages 557–566, 1998.
• Serre, T. , Kouh, M. , Cadieu, C. , Knoblich, U. , Kreiman, G. , Poggio, T. , "A Theory of Object Recognition: Computations and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex", MIT-CSAIL-TR-2005-082.
• Beis, J. , and Lowe, D. G "Shape indexing using approximate nearest-neighbour search in high-dimensional spaces", Conference on Computer Vision and Pattern Recognition, Puerto Rico, 1997, pp. 1000–1006.
• K. Mikolajczyk and C. Schmid. "A Performance Evaluation of Local Descriptors". In Interna-tional Conference on Computer Vision and Pattern Recognition, volume 2, pages 257–263 jun 2003
• P. A. Viola and M. J. Jones. "Rapid object detection using a boosted cascade of simple features". In CVPR (1), pages 511 –518, 2001
• Luo Juan, Oubong Gwun, "A Comparison of SIFT, PCA-SIFT and SURF"
• Itti, L. , Koch, C. , & Niebur, E. "A model of saliency-based visual attention for rapid scene analysis". IEEE Transactions on Pattern Analysis and Machine Intelligence, 20, 1254–1259. 1998.