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Automatic function selection for large scale salient object detection

Published: 23 October 2006 Publication History

Abstract

Robust detection of a large dictionary of salient objects in natural image database is of fundamental importance to image retrieval systems. We review three popular frameworks for salient object detection, i.e., segmentation-based method, grid-based method and part-based method and discuss their advantages and limitations. We argue that using these frameworks individually is generally not enough to handle a large number of salient object classes accurately because of the intrinsic diversity of salient object features. Motivated by this observation, we have proposed a new system which combines the merits of these frameworks into one single hybrid system. The system automatically selects the appropriate modeling method for each individual object class using J measure and shape variance. We conduct comparison experiments on two popular image dataset -- Corel and LabelMe. Empirical results have shown that the proposed hybrid method is more general and can handle much more salient object classes in a robust manner.

References

[1]
Y. Rui, T. S. Huang, and S.-F. Chang, "Image Retrieval: Current Techniques, Promising Directions and OpenIssues", Journal of Visual Communication and Image Representation Vol. 10, pp.39--62, 1999.
[2]
F. Monay, D. Gatica-Perez, "On image auto-annotation with latent space models", ACM Multimedia, pp. 275--278, 2003.
[3]
A. W. M. Smeulders, M. Worring, S. Santini, A. Gupta and R. Jain, "Content-based image retrieval at the end of the early years", IEEE Trans. on PAMI vol. 22, pp. 1349--1380, 2000.
[4]
R. Zhao, W. I. Grosky, "Negotiating the semantic gap: from feature maps to semantic landscapes"Pattern Recognition vol. 35, no. 3, pp. 593--600, 2002.
[5]
X. He, W.-Y. Ma, O. King, M. Li and H. J. Zhang, "Learning and inferring a semantic space from user 's relevance feedback", ACM Multimedia, 2002.
[6]
R. Lienhart and A. Hartmann, "Classifying images on the web automatically", Journal of Electronic Imaging vol. 11, no. 4, pp. 445--454, 2002.
[7]
C. Carson, S. Belongie, H. Greenspan, J. Malik, "Blobworld: Image segmentation using expectation-maximization and its application to image querying", IEEE Trans. PAMI 2002.
[8]
Y. Gong, "Advancing Content-Based Image Retrieval by Exploiting Image Color and Region Features", Multimedia Systems vol. 7, no.6, pp. 449--457, 1999.
[9]
K. Vu, K. A. Hua, W. Tavanapong, "Image Retrieval Based on Regions of Interest", IEEE Trans. TKDE vol. 15, no.4, pp. 1045--1049,2003.
[10]
J. Z. Wang, J. Liand G. Wiederhold, "SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture Libraries", IEEE Trans. on PAMI vol. 23, no. 9, pp. 947--963, 2001.
[11]
J. R. Smith and C.-S. Li, "Image classification and querying using composite region template", Journal of CVIU 1999.
[12]
J. Fan, Y. Gao, H. Luo, "Multi-level annotation of natural scenes using dominant image components and semantic image concepts", ACM Multimedia, 2004.
[13]
F. Monay, D. Gatica-Perez, "PLSA-based image auto-annotation: constraining the latent space", ACM Multimedia, pp. 348--351, 2004.
[14]
N. Serrano, A. E. Savakis, J. Luo, "Improvedscene classification using efficient low-level features and semantic cues", Pattern Recognition vol.37,no.9, pp. 1773--1784, 2004.
[15]
R. Jin, A. G. Hauptmann, "Using a probabilistic source model for comparing images", ICIP, pp. 941--944, 2002.
[16]
A. Vailaya, M. Figueiredo, A. K. Jain, H. J. Zhang, "Image classification for content-based indexing", IEEE Trans. on Image Processing vol. 10, pp. 117--130, 2001.
[17]
D. Lowe, "Distinctive image features from scale-invariant keypoints", International Journal of Computer Vision 2004.
[18]
L. Fei-Fei, R. Fergus, P. Perona, "A Bayesian approach to unsupervised One-Shot learning of Object categories", IEEE ICCV, 2003.
[19]
Y. Deng, B. S. Manjunath, "Unsupervised Segmentation of Color-Texture Regions in Images and Video", IEEE Trans. on PAMI 2001.
[20]
Y. Freund, R. E. Schapire, "Experiments with a new boosting algorithm", Proc. ICML, pp. 148--156, 1996.
[21]
A. Torralba, K. Murphy, W. Freeman, "Sharing features: efficient boosting procedures for multiclass object detection", CVPR, 2004.
[22]
P. Viola, M. Jones, "Robust real-time face detection", Intl. J. Computer Vision vol. 57, no. 2, pp. 137--154, 2004.

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cover image ACM Conferences
MM '06: Proceedings of the 14th ACM international conference on Multimedia
October 2006
1072 pages
ISBN:1595934472
DOI:10.1145/1180639
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 23 October 2006

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  1. automatic image annotation
  2. salient object detection

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MM06
MM06: The 14th ACM International Conference on Multimedia 2006
October 23 - 27, 2006
CA, Santa Barbara, USA

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