Similar articles

Abstract: image feature optimization is an important means to deal with high-dimensional image data in image semantic understanding and its applications. We formulate image feature optimization as the establishment of a mapping between high- and low-dimensional space via a five-tuple model. nonlinear dimensionality reduction based on manifold learning provides a feasible way for solving such a problem. We propose a novel globular neighborhood based locally linear embedding (GNLLE) algorithm using neighborhood update and an incremental neighbor search scheme, which not only can handle sparse datasets but also has strong anti-noise capability and good topological stability. Given that the distance measure adopted in nonlinear dimensionality reduction is usually based on pairwise similarity calculation, we also present a globular neighborhood and path clustering based locally linear embedding (GNPCLLE) algorithm based on path-based clustering. Due to its full consideration of correlations between image data, GNPCLLE can eliminate the distortion of the overall topological structure within the dataset on the manifold. Experimental results on two image sets show the effectiveness and efficiency of the proposed algorithms.

[1] Abusham, E.E., Ngo, D., Teoh, A., 2005. Fusion of locally linear embedding and principal component analysis for face recognition (FLLEPCA). LNCS, 3687:326-333.

[2] Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J., 1997. Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell., 19(7):711-720.

[3] Belkin, M., Niyogi, P., 2001. Laplacian eigenmaps and spectral techniques for embedding and clustering. Adv. Neur. Inf. Process. Syst., 14:585-591.

[4] Belkin, M., Niyogi, P., 2003. Laplacian eigenmaps for dimensionality reduction and data representation. Neur. Comput., 15(6):1373-1396.

[5] Bellman, R., 1961. Adaptive Control Processes: A Guided Tour. Princeton University Press, New Jersey.

[6] Bezdek, J.C., 1981. Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer Academic Publishers, Norwell, MA, USA.

[7] Cao, S.M., Ye, S.W., 2007. A better scaled local line embedding algorithm. Comput. Simul., 24(5):87-90 (in Chinese).

[8] Chang, J.P., Shen, H.X., Zhou, Z.H., 2004. Unified locally linear embedding and linear discriminant analysis algorithm (ULLELDA) for face recognition. LNCS, 3338:296-304.

[9] Datta, R., Joshi, D., Li, J., Wang, J., 2008. Image retrieval: ideas, influences, and treads of the new age. ACM Trans. Comput. Surv., 40(2):5-60.

[10] de Juan, C., Bodenheimer, B., 2004. Cartoon Textures. Proc. Eurographics, ACM SIGGRAPH Symp. on Computer Animation, p.267-276.

[11] de Ridder, D., Kouropteva, O., Okum, O., Pietikäinen, M., Duin, R.P.W., 2003. Supervised locally linear embedding. LNCS, 2714:333-341.

[12] Dollár, P., Rabaud, V., Belongie, S., 2006. Learning to Traverse Image Manifolds. Proc. 12th Annual Conf. on Neural Information Processing Systems. Available from http://vision.ucsd.edu/~pdollar/research/papers/DollarRabaudBelongieNIPS06manifold.pdf [Accessed on Nov. 23, 2009].

[13] Dollár, P., Rabaud, V., Belongie, S., 2007. Non-isometric Manifold Learning: Analysis and an Algorithm. Proc. 24th Int. Conf. on Machine Learning, p.241-248.

[14] Dorai, C., Venkatesh, S., 2003. Bridging the semantic gap with computational media aesthetics. IEEE Multimedia, 10(2):15-17.

[15] Dunn, J.C., 1973. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Cybern. & Syst., 3(3):32-57.

[16] Fischer, B., Buchmann, J.M., 2003. Path-based clustering for grouping of smooth curves and texture segmentation. IEEE Trans. Pattern Anal. Mach. Intell., 25(4):513-518.

[17] Fischer, B., Zőller, T., Buhmann, J.M., 2001. Path based pairwise data clustering with application to texture segmentation. LNCS, 2134:235-250.

[18] Hofmann, T., Buhmann, J.M., 1997. Pairwise data clustering by deterministic annealing. IEEE Trans. Pattern Anal. Mach. Intell., 19(1):1-14.

[19] Hua, Z.G., Wang, X.J., Liu, Q.S., Lu, H.Q., 2005. Semantic Knowledge Extraction and Annotation for Web Images. Proc. 13th Annual ACM Int. Conf. on Multimedia, p.467-470.

[20] Jeon, J., Lavrenko, V., Manmatha, R., 2003. Automatic Image Annotation and Retrieval Using Cross-media Relevance Models. Proc. 26th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, p.119-126.

[21] Jiang, Z.W., 2007. Research on Content-based Web Image Filter Technology. PhD Thesis, Zhejiang University, Hangzhou, China (in Chinese).

[22] Jin, H., Ooi, B.C., Shen, H.T., Yu, C., Zhou, A.Y., 2003. An Adaptive and Efficient Dimensionality Reduction Algorithm for High-dimensional Indexing. Proc. IEEE 19th Int. Conf. on Data Engineering, p.87-98.

[23] Jolliffe, I.T., 1986. Principal Component Analysis. Springer-Verlag, New York.

[24] Krishnapuram, B., Carin, L., Hartemink, A.J., 2004. Joint classifier and feature optimization for comprehensive cancer diagnosis using gene expression data. J. Comput. Biol., 11(2-3):227-242.

[25] Li, H., Du, S.D., Lu, F., Gao, D.T., 2006. Feature extraction and image reconstruction of video sequence based on nonlinear dimensionality reduction algorithms. Pattern Recogn. Artif. Intell., 19(5):646-651 (in Chinese).

[26] Platt, J., 1999. Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines. Technical Report MSR-TR-98-14, Microsoft Research, USA.

[27] Roweis, S.T., Saul, L.K., 2000. Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500):2323-2326.

[28] Saul, L.K., Roweis, S.T., 2003. Think globally, fit locally: unsupervised learning of low dimensional manifolds. J. Mach. Learn. Res., 4(2):119-155.

[29] Seung, H.S., Lee, D., 2000. The manifold ways of perception. Science, 290(5500):2268-2269.

[30] Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R., 2000. Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Mach. Intell., 22(12):1349-1380.

[31] Sweis, D.L., Weng, J., 1996. Using discriminant eigenfeatures for image retrieval. IEEE Trans. Pattern Anal. Mach. Intell., 18(3):831-836.

[32] Tenenbaum, J.B., de Silva, V., Langford, J.C., 2000. A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500):2319-2323.

[33] Turk, M.A., Pentand, A.P., 1991. Face Recognition Using Eigenfaces. Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, p.586-591.

[34] Vapnik, V., 1995. The Nature of Statistical Learning Theory. Springer-Verlag, New York.

[35] Wang, H., Tian, N., Zhang, X.M., Feng, X.A., Zhao, N., 2003. Alternate Feature Optimization for 3-Class Underwater Target Recognition Based on SVM Classifiers. Proc. IEEE Int. Conf. on Neural Network and Signal Processing, p.144-148.

[36] Wang, H.Y., Zheng, J., Yao, Z.A., Li, L., 2006. Application of dimension reduction on using improved LLE based on clustering. J. Comput. Res. Dev., 43(8):1485-1490 (in Chinese).

[37] Wang, J., Li, J., Wiederhold, G., 2001. Simplicity: semantics-sensitive integrated matching for picture libraries. IEEE Trans. Pattern Anal. Mach. Intell., 23(9):947-963.

[38] Weinberger, K.Q., Saul, L.K., 2004. Unsupervised Learning of Image Manifolds by Semidefinite Programming. Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, 2:988-995.

[39] Wu, Y.M., Chan, K.L., Wang, L., 2004. Face Recognition Based on Discriminative Manifold Learning. Proc. 17th Int. Conf. on Pattern Recognition, p.171-174.

[40] Xu, Z.J., Yang, J., Wang, M., 2004. A new nonlinear dimensionality reduction for color image. J. Shanghai Jiao Tong Univ., 38(12):2063-2072 (in Chinese).

[41] Yang, X.M., Wu, W., He, X.H., Chen, M., Xue, L., 2007. Handwritten numeral recognition based on manifold learning. J. Optoelectron. Las., 18(12):1478-1481 (in Chinese).

[42] Yao, L.Q., Tao, Q., 2005. One kind of manifold learning method for classification. Pattern Recogn. Artif. Intell., 5:541-545 (in Chinese).

[43] Yin, H.J., 2007. Nonlinear dimensionality reduction and data visualization: a review. Int. J. Autom. Comput., 4(3):294-303.

[44] Zhang, Q.N., Izquierdo, E., 2006. A Multi-feature Optimization Approach to Object-based Image Classification. Proc. 5th Int. Conf. on Image and Video Retrieval, p.310-319.