Evaluation strategies for image understanding and retrieval

We address evaluation of image understanding and retrieval large scale image data in the context of three evaluation projects. The first project is a comprehensive strategy for evaluating image retrieval algorithms and provides an open reference data set for doing so. The second project develops word prediction as a semantically relevant evaluation strategy, and applies it to the evaluation of of image processing methods for semantic image analysis. The third project evaluates words for suitability of their visual properties for use in an image annotation framework.

[1]  S. Sclaroff,et al.  ImageRover: a content-based image browser for the World Wide Web , 1997, 1997 Proceedings IEEE Workshop on Content-Based Access of Image and Video Libraries.

[2]  Joshua R. Smith,et al.  Image retrieval evaluation , 1998, Proceedings. IEEE Workshop on Content-Based Access of Image and Video Libraries (Cat. No.98EX173).

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

[4]  B. S. Manjunath,et al.  NeTra: A toolbox for navigating large image databases , 1997, Multimedia Systems.

[5]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[6]  Alex Pentland,et al.  Photobook: Content-based manipulation of image databases , 1996, International Journal of Computer Vision.

[7]  Bernt Schiele,et al.  On Performance Characterization and Optimization for Image Retrieval , 2002, ECCV.

[8]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[9]  Peter G. B. Enser,et al.  Progress in Documentation Pictorial Information Retrieval , 1995, J. Documentation.

[10]  R. Manmatha,et al.  Automatic image annotation and retrieval using cross-media relevance models , 2003, SIGIR.

[11]  David A. Forsyth,et al.  Benchmarks for storage and retrieval in multimedia databases , 2001, IS&T/SPIE Electronic Imaging.

[12]  Jitendra Malik,et al.  A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[13]  S. Sclaroff,et al.  Combining textual and visual cues for content-based image retrieval on the World Wide Web , 1998, Proceedings. IEEE Workshop on Content-Based Access of Image and Video Libraries (Cat. No.98EX173).

[14]  Ingemar J. Cox,et al.  The Bayesian image retrieval system, PicHunter: theory, implementation, and psychophysical experiments , 2000, IEEE Trans. Image Process..

[15]  T. Coleman,et al.  On the Convergence of Reflective Newton Methods for Large-scale Nonlinear Minimization Subject to Bounds , 1992 .

[16]  Thomas F. Coleman,et al.  A Reflective Newton Method for Minimizing a Quadratic Function Subject to Bounds on Some of the Variables , 1992, SIAM J. Optim..

[17]  Gerard Salton,et al.  The State of Retrieval System Evaluation , 1992, Inf. Process. Manag..

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

[19]  Nando de Freitas,et al.  A Statistical Model for General Contextual Object Recognition , 2004, ECCV.

[20]  David A. Forsyth,et al.  Clustering art , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[21]  David A. Forsyth,et al.  Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary , 2002, ECCV.

[22]  Thomas F. Coleman,et al.  On the convergence of interior-reflective Newton methods for nonlinear minimization subject to bounds , 1994, Math. Program..

[23]  Peter Stanchev,et al.  Content-Based Image Retrieval Systems , 2001 .

[24]  Kobus Barnard,et al.  Evaluating image retrieval , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

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

[26]  F. T. Wright,et al.  Order restricted statistical inference , 1988 .

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

[28]  C. Holmes,et al.  Generalized monotonic regression using random change points , 2003, Statistics in medicine.

[29]  Thomas Pfund,et al.  Dynamic multimedia annotation tool , 2001, IS&T/SPIE Electronic Imaging.

[30]  Eero Sormunen,et al.  End-User Searching Challenges Indexing Practices in the Digital Newspaper Photo Archive , 2004, Information Retrieval.

[31]  Keiji Yanai,et al.  Image region entropy: a measure of "visualness" of web images associated with one concept , 2005, MULTIMEDIA '05.

[32]  M. Schell,et al.  The Reduced Monotonic Regression Method , 1997 .

[33]  David A. Forsyth,et al.  The effects of segmentation and feature choice in a translation model of object recognition , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[34]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[35]  James Ze Wang,et al.  Learning-based linguistic indexing of pictures with 2--d MHMMs , 2002, MULTIMEDIA '02.

[36]  R. Manmatha,et al.  Using Maximum Entropy for Automatic Image Annotation , 2004, CIVR.

[37]  Sudeep Sarkar,et al.  A Framework for Performance Characterization of Intermediate-Level Grouping Modules , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[38]  Jitendra Malik,et al.  Learning to detect natural image boundaries using local brightness, color, and texture cues , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[39]  Peter G. B. Enser,et al.  Analysis of user need in image archives , 1997, J. Inf. Sci..

[40]  David A. Forsyth,et al.  Matching Words and Pictures , 2003, J. Mach. Learn. Res..

[41]  Neil J. Gunther,et al.  Benchmark for image retrieval using distributed systems over the Iinternet: BIRDS-I , 2000, IS&T/SPIE Electronic Imaging.

[42]  B. S. Manjunath,et al.  Unsupervised Segmentation of Color-Texture Regions in Images and Video , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[43]  James Ze Wang,et al.  SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[44]  R. Manmatha,et al.  A Model for Learning the Semantics of Pictures , 2003, NIPS.