Feature integration analysis of bag-of-features model for image retrieval

Neurocomputing

Volumn 120, Issue , 2013, Pages 355-364

  Yu, Jing ^a   Qin, Zengchang ^a   Wan, Tao ^b   Zhang, Xi ^a  

^a BEIHANG UNIVERSITY   (China)

^b BOSTON UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

Bag of features (BoF); Histogram intersection; HOG LBP; Image retrieval; SIFT LBP; Weighted K means

Indexed keywords

BAG-OF-FEATURES; HISTOGRAM INTERSECTION; HOG-LBP; SIFT-LBP; WEIGHTED K-MEANS;

CONTENT BASED RETRIEVAL; IMAGE RETRIEVAL; SEMANTICS;

INTEGRATION;

ACCURACY; ARTICLE; BAG OF FEATURE MODEL; COMPARATIVE STUDY; CONTROLLED STUDY; DATA BASE; EXPERIMENTAL STUDY; FEATURE INTEGRATION ANALYSIS; IMAGE ANALYSIS; IMAGE BASED HISTOGRAM OF ORIENTED GRADIENT LOCAL BINARY PATTERN FEATURE INTEGRATION; IMAGE BASED SCALE INVARIANT FEATURE TRANSFORM LOCAL BINARY PATTERN FEATURE INTEGRATION; IMAGE QUALITY; IMAGE RETRIEVAL; K NEAREST NEIGHBOR; LEARNING ALGORITHM; NONBIOLOGICAL MODEL; PATCH BASED HISTOGRAM OF ORIENTED GRADIENT LOCAL BINARY PATTERN FEATURE INTEGRATION; PATCH BASED SCALE INVARIANT FEATURE TRANSFORM LOCAL BINARY PATTERN FEATURE INTEGRATION; PRIORITY JOURNAL; QUALITATIVE ANALYSIS; QUALITY CONTROL; QUANTITATIVE ANALYSIS;

EID: 84882902606     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2012.08.061     Document Type: Article

References (33)

1. Pang Y., Shang M., Yuan Y., Pan J. Scale invariant image matching using triplewise constraint and weighted voting. Neurocomputing 2012, 83:64-71.
2. Datta R., Joshi D., Li J., Wang J.Z. Image retrieval: ideas, influences, and trends of the new age. ACM Comput. Surv. 2008, 40:1-60.
3. X. Yuan, J. Yu, Z. Qin, T. Wan, A SIFT-LBP image retrieval model based on bag-of-features, in: ICIP, 2011, pp. 1161-1164.
4. J. Pujari, P.S. Hiremath, Content based image retrieval using color, texture and shape features, in: Proceedings of the International Conference on Advanced Computing and Communications, 2007, pp. 780-784.
5. Wan T., Qin Z. A new technique for summarizing video sequences through histogram evolution. SPCOM 2010, 1-5.
6. Yan H., Yuan Y., Wang K. Robust CoHOG feature extraction in human-centered image/video management system. IEEE Trans. Syst. Man Cybernet. Part B: Cybernet. 2012, 42:458-468.
7. Tamura H., Mori S., Yamawaki T. Texture features corresponding to visual perception. IEEE Trans. Syst. Man Cybernet. 1978, 8:460-473.
8. M. Pietikäinen, T. Ahonen, V. Takala, Block-based methods for image retrieval using local binary patterns, in: Proceedings of the 14th Scandinavian Conference on Image Analysis, 2005, pp. 882-891.
9. D.G. Lowe, Object recognition from local scale-invariant features, in: ICCV, 1999, pp. 1150-1157.
10. Y. Ke, R. Sukthankar, PCA-SIFT: a more distinctive representation for local image descriptors, in: CVPR 85, 2004, pp. 506-513.
11. Bay H., Ess A., Tuytelaars T., Gool L.V. SURF: speeded-up robust features. Comput. Vis. Image Understand. 2008, 110:346-359.
12. Pang Y., Li W., Yuan Y., Pan J. Fully affine invariant SURF for image matching. Neurocomputing 2012, 85:6-10.
13. Pang Y., Yuan Y., Li X., Pan J. Efficient HOG human detection. Signal Process. 2011, 91:773-781.
14. Ojala D., Pietikäine M., Harwood D. A comparative study of texture measures with classification based on feature distributions. Pattern Recognition 1996, 19:51-59.
15. Ojala D., Pietikäinen M., Mäenpää T. Multiresolution gray scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 2002, 24:971-987.
16. X. Wang, T. Han, S. Yan, An HOG-LBP human detector with partial occlusion handling, in: ICCV, 2009, pp. 32-39.
17. Heikkilä M., Pietikäinen M., Schmid C. Description of interest regions with local binary patterns. Pattern Recognition 2009, 42:425-436.
18. Zheng Y., Huang X., Feng S. An image matching algorithm based on combination of SIFT and rotation invariant LBP. J. Computer-Aided Design Comput. Graph. 2010, 2:286-292.
19. W.R. Schwartz, A. Kembhavi, D. Harwood, L.S. Davis, Huam detection using partial least squares analysis, in: ICCV, 2009, pp. 24-31.
20. J. Fehr, A. Streicher, H. Burkhardt, A bag of features approach for 3D shape retrieval, in: Proceedings of the Fifth International Symposium on Advances in Visual Computing: Part I, 2009, pp. 34-43.
21. B. Triggs, F. Jurie, Creating efficient codebooks for visual recognition, in: ICCV, vol. 1, 2005, pp. 604-610.
22. Z. Qin, M. Thint, Z. Huang, Ranking answers by hierarchical topic models, in: Proceedings of IEA/AIE LNCS, vol. 5579, 2009, pp. 103-112.
23. Tian Q., Zhang S. Descriptive visual words and visual phrases for image applications. ACM Multimedia 2009, 19-24.
24. C. Schmid, K. Mikolajczyk, A performance evaluation of local descriptors, in: ICPR, vol. 2, 2003, pp. 257-263.
25. N. Dalal, B. Triggs, Histograms of oriented gradients for human detection, in: CVPR, vol. 1, 2005, pp. 886-893.
26. N. Dalal, B. Triggs, C. Schmid, Human detection using oriented histograms of flow and appearance, in: ECCV, 2006, pp. 428-441.
27. Felzenszwalb P.F., Girshick R.B., McAllester D., Ramanan D. Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 2010, 32:1627-1645.
28. P. Sabzmeydani, G. Mori, Detecting pedestrians by learning shapelet features, in: CVPR, 2007, pp. 1-8.
29. Q. Zhu, M.C. Yeh, K.T. Cheng, S. Avidan, Fast human detection using a cascade of histograms of oriented gradients, in: CVPR, 2006, pp. 1491-1498.
30. Wang J.A., Li J., Wiederhold G. SIMPLIcity: semantics-sensitive integrated matching for picture libraries. IEEE Trans. Pattern Anal. Mach. Intell. 2001, 23:947-963.
31. J.Z. Wang Group: Modeling Objects, Concepts, and Aesthetics in Images, website: 〈〉. http://wang.ist.psu.edu/.
32. X. Wang, A novel circular ring histogram for content-based image retrieval, ETCS, 2009, pp. 1-4.
33. J. Yang, K. Yu, Y. Gong, T. Huang, Linear spatial pyramid matching using sparse coding for image classification, in: CVPR, 2009, pp. 1794-1801.