Correlative Linear Neighborhood Propagation for Video Annotation

Recently, graph-based semisupervised learning methods have been widely applied in multimedia research area. However, for the application of video semantic annotation in multilabel setting, these methods neglect an important characteristic of video data: The semantic concepts appear correlatively and interact naturally with each other rather than exist in isolation. In this paper, we adapt this semantic correlation into graph-based semisupervised learning and propose a novel method named correlative linear neighborhood propagation to improve annotation performance. Experiments conducted on the Text REtrieval Conference VIDeo retrieval evaluation data set have demonstrated its effectiveness and efficiency.

[1]  Tat-Seng Chua,et al.  Integrated graph-based semi-supervised multiple/single instance learning framework for image annotation , 2008, ACM Multimedia.

[2]  Fei Wang,et al.  Label Propagation through Linear Neighborhoods , 2008, IEEE Trans. Knowl. Data Eng..

[3]  Tao Mei,et al.  Video annotation based on temporally consistent Gaussian random field , 2007 .

[4]  Xuelong Li,et al.  Geometric Mean for Subspace Selection , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Alexander Zien,et al.  Semi-Supervised Learning , 2006 .

[6]  Meng Wang,et al.  Manifold-ranking based video concept detection on large database and feature pool , 2006, MM '06.

[7]  Gerhard Weikum,et al.  Graph-based text classification: learn from your neighbors , 2006, SIGIR.

[8]  Tao Mei,et al.  Correlative multi-label video annotation , 2007, ACM Multimedia.

[9]  Jingrui He,et al.  Generalized Manifold-Ranking-Based Image Retrieval , 2006, IEEE Transactions on Image Processing.

[10]  Changhu Wang,et al.  Image annotation refinement using random walk with restarts , 2006, MM '06.

[11]  Xuelong Li,et al.  Discriminant Locally Linear Embedding With High-Order Tensor Data , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[12]  Wei-Ying Ma,et al.  An adaptive graph model for automatic image annotation , 2006, MIR '06.

[13]  Xian-Sheng Hua,et al.  Video Annotation Based on Kernel Linear Neighborhood Propagation , 2008, IEEE Transactions on Multimedia.

[14]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[15]  Ronald Rosenfeld,et al.  Semi-supervised learning with graphs , 2005 .

[16]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[17]  John R. Smith,et al.  Large-scale concept ontology for multimedia , 2006, IEEE MultiMedia.

[18]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[19]  Rong Jin,et al.  A graph-based framework for relation propagation and its application to multi-label learning , 2006, SIGIR.

[20]  Meng Wang,et al.  Correlative multilabel video annotation with temporal kernels , 2008, TOMCCAP.

[21]  Xuelong Li,et al.  General Tensor Discriminant Analysis and Gabor Features for Gait Recognition , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Zoubin Ghahramani,et al.  Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions , 2003, ICML 2003.

[23]  Bernhard Schölkopf,et al.  Learning with Local and Global Consistency , 2003, NIPS.

[24]  Meng Wang,et al.  Structure-sensitive manifold ranking for video concept detection , 2007, ACM Multimedia.