Content-Based Retrieval in Digital Libraries

This chapter is concerned with finding images or video from (possibly very large) collections of these. Each type of modality in multimedia information, e.g., text and image, provides its own type of semantic information to help in search for content. That is, text-based search is bolstered by information in images and video, from low-level features to high-level semantic content. In this book we focus only on techniques and systems that make use of image features themselves, without text, to retrieve images or video from databases or from the web. Detail is provided on specific features useful to this purpose. Search engines devised on these features are said to be content based: the search is guided by image similarity measures based on the statistical content of each image. At a higher semantic level,action recognition in video is also examined in some detail.

[1]  Yuriy Reznik,et al.  On MPEG work towards a standard for visual search , 2011, Optical Engineering + Applications.

[2]  Bernd Girod,et al.  Mobile Visual Search , 2011, IEEE Signal Processing Magazine.

[3]  Baitao Li Chang,et al.  DPF - a perceptual distance function for image retrieval , 2002, Proceedings. International Conference on Image Processing.

[4]  Claudio Gutierrez,et al.  Survey of graph database models , 2008, CSUR.

[5]  Marcel Worring,et al.  Where Is the User in Multimedia Retrieval? , 2012, IEEE Multim..

[6]  Michael Isard,et al.  Total Recall: Automatic Query Expansion with a Generative Feature Model for Object Retrieval , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[7]  Hermann Ney,et al.  Features for image retrieval: an experimental comparison , 2008, Information Retrieval.

[8]  Mark S. Drew,et al.  Video keyframe production by efficient clustering of compressed chromaticity signatures (poster session) , 2000, ACM Multimedia.

[9]  Shu-Yuan Chen,et al.  Complementary retrieval for distorted images , 2002, Pattern Recognit..

[10]  Barbara Caputo,et al.  Recognizing human actions: a local SVM approach , 2004, ICPR 2004.

[11]  Jitendra Malik,et al.  Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  James Ze Wang,et al.  SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

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

[14]  Ze-Nian Li,et al.  Illumination color covariant locale-based visual object retrieval , 2002, Pattern Recognit..

[15]  Shih-Fu Chang,et al.  VideoQ: an automated content based video search system using visual cues , 1997, MULTIMEDIA '97.

[16]  Fang Liu,et al.  Periodicity, Directionality, and Randomness: Wold Features for Image Modeling and Retrieval , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Nicu Sebe,et al.  Content-based multimedia information retrieval: State of the art and challenges , 2006, TOMCCAP.

[18]  Thomas S. Huang,et al.  Relevance feedback: a power tool for interactive content-based image retrieval , 1998, IEEE Trans. Circuits Syst. Video Technol..

[19]  Ramakant Nevatia,et al.  Evaluating multimedia features and fusion for example-based event detection , 2013, Machine Vision and Applications.

[20]  Pietro Perona,et al.  A Bayesian hierarchical model for learning natural scene categories , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[21]  Amarnath Gupta,et al.  Virage video engine , 1997, Electronic Imaging.

[22]  Gang Wang,et al.  Learning Image Similarity from Flickr Groups Using Fast Kernel Machines , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Bernd Girod,et al.  Compressed Histogram of Gradients: A Low-Bitrate Descriptor , 2011, International Journal of Computer Vision.

[24]  Gerald L. Lohse,et al.  Towards a texture naming system: Identifying relevant dimensions of texture , 1993, Vision Research.

[25]  James Lee Hafner,et al.  Efficient Color Histogram Indexing for Quadratic Form Distance Functions , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  Tat-Seng Chua,et al.  From text question-answering to multimedia QA on web-scale media resources , 2009, LS-MMRM '09.

[27]  Yongwang Zhao,et al.  A New Query Dependent Feature Fusion Approach for Medical Image Retrieval based on One-Class SVM , 2011 .

[28]  Suh-Yin Lee,et al.  Retrieval of similar pictures on pictorial databases , 1991, Pattern Recognit..

[29]  James Ze Wang,et al.  Image retrieval: Ideas, influences, and trends of the new age , 2008, CSUR.

[30]  Adrian Hilton,et al.  Shape Similarity for 3D Video Sequences of People , 2010, International Journal of Computer Vision.

[31]  Dragutin Petkovic,et al.  Query by Image and Video Content: The QBIC System , 1995, Computer.

[32]  Remco C. Veltkamp,et al.  A survey of content based 3D shape retrieval methods , 2004, Proceedings Shape Modeling Applications, 2004..

[33]  Ming Yang,et al.  Query Specific Fusion for Image Retrieval , 2012, ECCV.

[34]  Jie Wei,et al.  Illumination-invariant image retrieval and video segmentation , 1999, Pattern Recognit..

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

[36]  David A. Forsyth,et al.  Finding Naked People , 1996, ECCV.

[37]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[38]  Shih-Fu Chang,et al.  Integration of Visual and Text-Based Approaches for the Content Labeling and Classification of Photographs , 1999, SIGIR 1999.

[39]  Michael Isard,et al.  Object retrieval with large vocabularies and fast spatial matching , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[40]  Krista A. Ehinger,et al.  SUN database: Large-scale scene recognition from abbey to zoo , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[41]  Tom Minka,et al.  Modeling user subjectivity in image libraries , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[42]  Pooja,et al.  An effective image retrieval using the fusion of global and local transforms based features , 2012 .

[43]  Yiannis S. Boutalis,et al.  Investigating the Behavior of Compact Composite Descriptors in Early Fusion, Late Fusion and Distributed Image Retrieval , 2010 .

[44]  Ze-Nian Li,et al.  Illumination Invariance and Object Model in Content-Based Image and Video Retrieval , 1999, J. Vis. Commun. Image Represent..

[45]  Greg Mori,et al.  Social roles in hierarchical models for human activity recognition , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

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

[47]  Michael J. Swain,et al.  Color indexing , 1991, International Journal of Computer Vision.

[48]  David A. Forsyth,et al.  Learning the semantics of words and pictures , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[49]  Thorsten Joachims,et al.  Training linear SVMs in linear time , 2006, KDD '06.

[50]  Qi Tian,et al.  Spatial coding for large scale partial-duplicate web image search , 2010, ACM Multimedia.

[51]  Hideyuki Tamura,et al.  Textural Features Corresponding to Visual Perception , 1978, IEEE Transactions on Systems, Man, and Cybernetics.