An experimental comparison of three guiding principles for the detection of salient image locations: Stability, complexity, and discrimination

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4840 LNAI, Issue , 2007, Pages 184-197

  Gao, Dashan ^a   Vasconcelos, Nuno ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTATIONAL COMPLEXITY; DISCRETE COSINE TRANSFORMS; WAVELET DECOMPOSITION;

DISCRIMINANT SALIENCY; IMAGE TRANSFORMATIONS;

IMAGE ANALYSIS;

EID: 38549140065     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-77343-6_12     Document Type: Conference Paper

References (22)

1. Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey Vision Conference, pp. 147-151 (1988)
2. Förstner, W.: A framework for low level feature ex-traction. In: Eklundh, J.-O. (ed.) ECCV 1994. LNCS, vol. 800, pp. 383-394, Springer, Heidelberg (1994)
3. Sha'ashua, A., Ullman, S.: Structural saliency: the detection of globally salient structures using a locally connected network. In: Proc. ICCV, pp. 321-327 (1988)
4. Lindeberg, T.: Scale-space theory: A basic tool for analyzing structures at different scales. J. Applied Statistics 21(2), 224-270 (1994)
5. Mikolajczyk, K., Schmid, C.: Indexing based on scale invariant interest points. In: Proc. ICCV, pp. 525-531 (2001)
6. Mikolajczyk, K., Schmid, C.: An affine invariant interest point detector. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 128-142, Springer, Heidelberg (2002)
7. Lowe, D.G.: Object recognition from local scale-invariant features. In: Proc. ICCV, pp. 1150-1157 (1999)
8. Sebe, N., Lew, M.S.: Comparing salient point detectors. Pattern Recognition Letters 24(1-3), 89-96 (2003)
9. Kadir, T., Brady, M.: Scale, saliency and image description, Int'l. J. Comp. Vis. 45, 83-105 (2001)
10. Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. PAMI 20(11) (1998)
11. Privitera, C., Stark, L.: Algorithms for defining visual regions-of-interest: comparison with eye fixations. IEEE Trans. PAMI 22, 970-982 (2000)
12. Gao, D., Vasconcelos, N.: Discriminant saliency for visual recognition from cluttered scenes. In: Proc. NIPS, pp. 481-488 (2004)
13. Walker, K., Cootes, T., Taylor, C.: Locating salient object features. In: Proc. British Machine Vision Conf., pp. 557-566 (1998)
14. Schiele, B., Crowley, J.: Where to look next and what to look for. In: Intelligent Robots and Systems (IROS), pp. 1249-1255 (1996)
15. Burt, P., Adelson, E.H.: The laplacian pyramid as a compact image code. IEEE Trans. Communication COM-31, 532-540 (1983)
16. Schmid, C., Mohr, R., Bauckhage, C.: Evaluation of interest point detectors. Int'l J. Comp. Vis. 37(2), 151-172 (2000)
17. Fergus, R., Perona, P., Zisserman, A.: Object class recognition by unsupervised scale-invariant learning. In: Proc. IEEE Conf. CVPR (2003)
18. Kadir, T., Zisserman, A., Brady, M.: An affine invariant saliency region detector. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 228-241, Springer, Heidelberg (2004)
19. Nene, S., Nayar, S., Murase, H.: Columbia object image library: Coil-100. Technical Report CUCS-006-96, Dept. of Computer Science, Columbia Univ. (1996)
20. Vasconcelos, N., Carneiro, G.: What is the role of independence for visual regognition? In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, Springer, Heidelberg (2002)
21. Daugman, J.: Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. J. Opt. Soc. Am. A 2(7), 1362-1373 (1985)
22. nd Int. Workshop on Statistical and Computational Theories of Vision Modeling, Learning, Computing and Sampling (July 2001)