Image indexing and retrieval using an ART‐2A neural network architecture

Traditional content‐based image retrieval (CBIR) systems use low‐level features such as colors, shapes, and textures of images. Although, users make queries based on semantics, which are not easily related to such low‐level characteristics. Recent works on CBIR confirm that researchers have been trying to map visual low‐level characteristics and high‐level semantics. The relation between low‐level characteristics and image textual information has motivated this article which proposes a model for automatic classification and categorization of words associated to images. This proposal considers a self‐organizing neural network architecture, which classifies textual information without previous learning. Experimental results compare the performance results of the text‐based approach to an image retrieval system based on low‐level features. © 2008 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 18, 202–208, 2008

[1]  Xuelong Li,et al.  Which Components are Important for Interactive Image Searching? , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[2]  Degree of familiarity ART2 in knowledge-based landmine detection , 1999, IEEE Trans. Neural Networks.

[3]  Amit Mehrotra,et al.  Observations and problems applying ART2 for dynamic sensor pattern interpretation , 1996, IEEE Trans. Syst. Man Cybern. Part A.

[4]  Tsuhan Chen,et al.  Semantic propagation from relevance feedbacks , 2004, 2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763).

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

[6]  Thomas S. Huang,et al.  Optimizing learning in image retrieval , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[7]  Paul H. Lewis,et al.  Automatic Annotation of Images from the Practitioner Perspective , 2005, CIVR.

[8]  William I. Grosky,et al.  Negotiating the semantic gap: from feature maps to semantic landscapes , 2001, Pattern Recognit..

[9]  James F. Davis,et al.  Qualitative interpretation of sensor patterns , 1993, IEEE Expert.

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

[11]  Kobus Barnard,et al.  Word sense disambiguation with pictures , 2003, HLT-NAACL 2003.

[12]  Edward Y. Chang,et al.  Support vector machine active learning for image retrieval , 2001, MULTIMEDIA '01.

[13]  S LewMichael.  Next-Generation Web Searches for Visual Content , 2000 .

[14]  Matthieu Cord,et al.  RETIN: A Content-Based Image Indexing and Retrieval System , 2001, Pattern Analysis & Applications.

[15]  Laurence T. Yang,et al.  An On‐Line Approach for Classifying and Extracting Application Behavior on Linux , 2006 .

[16]  Michael S. Lew.  Next-Generation Web Searches for Visual Content , 2000, Computer.

[17]  Thomas S. Huang,et al.  Relevance feedback in image retrieval: A comprehensive review , 2003, Multimedia Systems.

[18]  Matthieu Cord,et al.  Interactive Exploration for Image Retrieval , 2005, EURASIP J. Adv. Signal Process..

[19]  Matthieu Cord,et al.  A comparison of active classification methods for content-based image retrieval , 2004, CVDB '04.

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

[21]  Stephen Grossberg,et al.  ART 2-A: an adaptive resonance algorithm for rapid category learning and recognition , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[22]  Thomas S. Huang,et al.  Relevance feedback techniques in interactive content-based image retrieval , 1997, Electronic Imaging.

[23]  Gail A. Carpenter,et al.  ART neural networks for remote sensing: vegetation classification from Landsat TM and terrain data , 1997, IEEE Trans. Geosci. Remote. Sens..

[24]  Wei-Ying Ma,et al.  Learning a semantic space from user's relevance feedback for image retrieval , 2003, IEEE Trans. Circuits Syst. Video Technol..

[25]  Ah-Hwee Tan,et al.  Modified ART 2A growing network capable of generating a fixed number of nodes , 2004, IEEE Transactions on Neural Networks.

[26]  Mingjing Li,et al.  Learning in hidden annotation-based image retrieval , 2004, ICPR 2004.

[27]  Xuelong Li,et al.  Multitraining Support Vector Machine for Image Retrieval , 2006, IEEE Transactions on Image Processing.

[28]  Thomas S. Huang,et al.  Unifying Keywords and Visual Contents in Image Retrieval , 2002, IEEE Multim..

[29]  Xuelong Li,et al.  Negative Samples Analysis in Relevance Feedback , 2007, IEEE Transactions on Knowledge and Data Engineering.

[30]  Stephen Grossberg,et al.  Art 2: Self-Organization Of Stable Category Recognition Codes For Analog Input Patterns , 1988, Other Conferences.

[31]  Kim-Teng Lua,et al.  Chinese character classification using an adaptive resonance network , 1992, Pattern Recognit..

[32]  Stephen Grossberg,et al.  The ART of adaptive pattern recognition by a self-organizing neural network , 1988, Computer.

[33]  Qi Tian,et al.  Integrating unlabeled images for image retrieval based on relevance feedback , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[34]  Donna K. Harman,et al.  Relevance feedback revisited , 1992, SIGIR '92.

[35]  Luis Rabelo,et al.  Sensor signal analysis by neural networks for surveillance in nuclear reactors , 1992 .

[36]  Jianping Fan,et al.  Automatic image annotation by using concept-sensitive salient objects for image content representation , 2004, SIGIR '04.

[37]  Jan C. van Gemert,et al.  Retrieving Images as Text , 2003 .

[38]  Howard C. Card,et al.  Vector quantization of images using modified adaptive resonance algorithm for hierarchical clustering , 2001, IEEE Trans. Neural Networks.