MultiMedia Modeling

The two-volume set LNCS 9516 and 9517 constitutes the thoroughly refereed proceedings of the 22nd International Conference on Multimedia Modeling, MMM 2016, held in Miami, FL, USA, in January 2016. The 32 revised full papers and 52 poster papers were carefully reviewed and selected from 117 submissions. In addition 20 papers were accepted for five special sessions out of 38 submissions as well as 7 demonstrations (from 11 submissions) and 9 video showcase papers. The papers are organized in topical sections on video content analysis, social media analysis, object recognition and system, multimedia retrieval and ranking, multimedia representation, machine learning in multimedia, and interaction and mobile. The special sessions are: good practices in multimedia modeling; semantics discovery from multimedia big data; perception, aesthetics, and emotion in multimedia quality modeling; multimodal learning and computing for human activity understanding; and perspectives on multimedia analytics

[1]  Xuelong Li,et al.  Constrained Nonnegative Matrix Factorization for Image Representation , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  D. Ruderman The statistics of natural images , 1994 .

[3]  Koichi Shinoda,et al.  Event Detection by Velocity Pyramid , 2014, MMM.

[4]  Nikolay N. Ponomarenko,et al.  TID2008 – A database for evaluation of full-reference visual quality assessment metrics , 2004 .

[5]  Lina J. Karam,et al.  A No-Reference Objective Image Sharpness Metric Based on the Notion of Just Noticeable Blur (JNB) , 2009, IEEE Transactions on Image Processing.

[6]  Deyu Meng,et al.  Easy Samples First: Self-paced Reranking for Zero-Example Multimedia Search , 2014, ACM Multimedia.

[7]  Chih-Jen Lin,et al.  LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..

[8]  Bernhard Schölkopf,et al.  A Kernel Two-Sample Test , 2012, J. Mach. Learn. Res..

[9]  Jun Huan,et al.  Knowledge Transfer with Low-Quality Data: A Feature Extraction Issue , 2011, IEEE Transactions on Knowledge and Data Engineering.

[10]  Zhou Wang,et al.  No-Reference Quality Assessment of Contrast-Distorted Images Based on Natural Scene Statistics , 2015, IEEE Signal Processing Letters.

[11]  Alan C. Bovik,et al.  No-Reference Image Quality Assessment in the Spatial Domain , 2012, IEEE Transactions on Image Processing.

[12]  Mubarak Shah,et al.  High-level event recognition in unconstrained videos , 2013, International Journal of Multimedia Information Retrieval.

[13]  Cordelia Schmid,et al.  Coloring Local Feature Extraction , 2006, ECCV.

[14]  Zhou Wang,et al.  No-reference perceptual quality assessment of JPEG compressed images , 2002, Proceedings. International Conference on Image Processing.

[15]  G. Wahba,et al.  Some results on Tchebycheffian spline functions , 1971 .

[16]  Alan C. Bovik,et al.  No-reference quality assessment using natural scene statistics: JPEG2000 , 2005, IEEE Transactions on Image Processing.

[17]  Anthony Widjaja,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.

[18]  Christoph Schnörr,et al.  Learning Sparse Representations by Non-Negative Matrix Factorization and Sequential Cone Programming , 2006, J. Mach. Learn. Res..

[19]  Antonio Torralba,et al.  SIFT Flow: Dense Correspondence across Different Scenes , 2008, ECCV.

[20]  Alan C. Bovik,et al.  Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality , 2011, IEEE Transactions on Image Processing.

[21]  Klaus-Robert Müller,et al.  Efficient BackProp , 2012, Neural Networks: Tricks of the Trade.

[22]  Shie Mannor,et al.  Robustness and Regularization of Support Vector Machines , 2008, J. Mach. Learn. Res..

[23]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[24]  Eero P. Simoncelli,et al.  Nonlinear image representation using divisive normalization , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[25]  Yoshua Bengio,et al.  Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.

[26]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[27]  Xiaojun Wu,et al.  Blind Image Quality Assessment Using a General Regression Neural Network , 2011, IEEE Transactions on Neural Networks.

[28]  Alan C. Bovik,et al.  Making a “Completely Blind” Image Quality Analyzer , 2013, IEEE Signal Processing Letters.

[29]  Yoshua Bengio,et al.  Greedy Layer-Wise Training of Deep Networks , 2006, NIPS.

[30]  Georges Quénot,et al.  TRECVID 2015 - An Overview of the Goals, Tasks, Data, Evaluation Mechanisms and Metrics , 2011, TRECVID.

[31]  Yuan Yan Tang,et al.  Multiview Hessian discriminative sparse coding for image annotation , 2013, Comput. Vis. Image Underst..

[32]  Deyu Meng,et al.  Bridging the Ultimate Semantic Gap: A Semantic Search Engine for Internet Videos , 2015, ICMR.

[33]  Jianguo Zhang,et al.  The PASCAL Visual Object Classes Challenge , 2006 .

[34]  Christophe Charrier,et al.  Blind Image Quality Assessment: A Natural Scene Statistics Approach in the DCT Domain , 2012, IEEE Transactions on Image Processing.

[35]  Luc Van Gool,et al.  Efficient Mining of Frequent and Distinctive Feature Configurations , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[36]  Yihong Gong,et al.  Linear spatial pyramid matching using sparse coding for image classification , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[37]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[38]  Alan C. Bovik,et al.  A Two-Step Framework for Constructing Blind Image Quality Indices , 2010, IEEE Signal Processing Letters.

[39]  Weisi Lin,et al.  Perceptual visual quality metrics: A survey , 2011, J. Vis. Commun. Image Represent..

[40]  Jorge Nocedal,et al.  On the limited memory BFGS method for large scale optimization , 1989, Math. Program..

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

[42]  Michael I. Jordan,et al.  A Robust Minimax Approach to Classification , 2003, J. Mach. Learn. Res..

[43]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[44]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[45]  Bernhard Schölkopf,et al.  A Generalized Representer Theorem , 2001, COLT/EuroCOLT.

[46]  Alexander J. Smola,et al.  Second Order Cone Programming Approaches for Handling Missing and Uncertain Data , 2006, J. Mach. Learn. Res..

[47]  Lei Zhang,et al.  Blind Image Quality Assessment Using Joint Statistics of Gradient Magnitude and Laplacian Features , 2014, IEEE Transactions on Image Processing.