Descriptor optimization for multimedia indexing and retrieval

[1]  Thomas Mensink,et al.  Image Classification with the Fisher Vector: Theory and Practice , 2013, International Journal of Computer Vision.

[2]  Georges Quénot,et al.  Quaero at TRECVID 2012: Semantic Indexing , 2012, TRECVID.

[3]  Hervé Jégou,et al.  Negative Evidences and Co-occurences in Image Retrieval: The Benefit of PCA and Whitening , 2012, ECCV.

[4]  Cordelia Schmid,et al.  Aggregating Local Image Descriptors into Compact Codes , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Georges Quénot,et al.  Two-layers re-ranking approach based on contextual information for visual concepts detection in videos , 2012, 2012 10th International Workshop on Content-Based Multimedia Indexing (CBMI).

[6]  Georges Quénot,et al.  Re-ranking by local re-scoring for video indexing and retrieval , 2011, CIKM '11.

[7]  Matthieu Cord,et al.  SALSAS: Sub-linear active learning strategy with approximate k-NN search , 2011, Pattern Recognit..

[8]  Bernard Mérialdo,et al.  Saliency moments for image categorization , 2011, ICMR.

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

[10]  Sabin Tiberius Strat,et al.  IRIM at TRECVID 2014: Semantic Indexing and Instance Search , 2014, TRECVID.

[11]  Thomas Mensink,et al.  Improving the Fisher Kernel for Large-Scale Image Classification , 2010, ECCV.

[12]  Koen E. A. van de Sande,et al.  Evaluating Color Descriptors for Object and Scene Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Georges Quénot,et al.  Active learning with multiple classifiers for multimedia indexing , 2010, 2010 International Workshop on Content Based Multimedia Indexing (CBMI).

[14]  Cordelia Schmid,et al.  Aggregating local descriptors into a compact image representation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[15]  Georges Quénot,et al.  Evaluations of multi-learner approaches for concept indexing in video documents , 2010, RIAO.

[16]  Stéphane Ayache,et al.  IRIM at TRECVID 2010: High Level Feature Extraction and Instance Search , 2010 .

[17]  C. Schmid,et al.  On the burstiness of visual elements , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Jun Yang,et al.  (Un)Reliability of video concept detection , 2008, CIVR '08.

[19]  Koen E. A. van de Sande,et al.  Evaluation of color descriptors for object and scene recognition , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[20]  Rajat Raina,et al.  Efficient sparse coding algorithms , 2006, NIPS.

[21]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[22]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[23]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[24]  Antonio Torralba,et al.  Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope , 2001, International Journal of Computer Vision.

[25]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[26]  Gabriela Csurka,et al.  Visual categorization with bags of keypoints , 2002, eccv 2004.

[27]  Andrew Zisserman,et al.  Video Google: a text retrieval approach to object matching in videos , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[28]  P. Duygulu,et al.  Visual categorization with bags of keypoints , 2002, eccv 2002.

[29]  M. Kramer.  Nonlinear principal component analysis using autoassociative neural networks , 1991 .

[30]  P. Mahalanobis.  On the generalized distance in statistics , 1936 .