Semantic clustering and querying on heterogeneous features for visual data

The ~ectiveness of the content-based image retrieval can be enhanced using the heterogeneous features embedded in the images. However, since the features in trxture, color, and shape are generated using dfierent computation methods and thus may requ-wedihent similarity measurements, the integration of the retrieval on heterogeneous features is a non-trivial task. In this paper, we present a semanticsbased clustering approach, termed SemQuery, to support visual queries on heterogeneous features of images. Using thii approach, the database images are classified based on their heterogeneous features. Each semantic image cluster contains a set of subclustem that are represented by the het~ogeneous features that the images contain. A database image is included into a feature subclnster only if the image containsall the features under the same cluster. We also designed a multi-layer model to merge the results of basic queries on individual features. A visual query processing strategy is then presented to support visual queries on heterogeneous features. ExTerirnental analysis is conducted and presented to demonstratee the effectiveness and efficiency oft he proposed approach.

[1]  Azriel Rosenfeld,et al.  Mosaic Models for Textures , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  James W. Modestino,et al.  Texture Discrimination Based Upon an Assumed Stochastic Texture Model , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Peter A. Lachenbruch,et al.  Classification: Methods for the Exploratory Analythi of Multivariate Data , 1982 .

[4]  Anil K. Jain,et al.  Markov Random Field Texture Models , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Antonin Guttman,et al.  R-trees: a dynamic index structure for spatial searching , 1984, SIGMOD '84.

[6]  W. Stromberg,et al.  A Fourier-Based Textural Feature Extraction Procedure , 1986, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[8]  Hans-Peter Kriegel,et al.  The R*-tree: an efficient and robust access method for points and rectangles , 1990, SIGMOD '90.

[9]  Peter J. Rousseeuw,et al.  Finding Groups in Data: An Introduction to Cluster Analysis , 1990 .

[10]  Jacek M. Zurada,et al.  Introduction to artificial neural systems , 1992 .

[11]  Ramesh C. Jain,et al.  A Visual Information Management System for the Interactive Retrieval of Faces , 1993, IEEE Trans. Knowl. Data Eng..

[12]  T. Kato,et al.  Rough sketch-based image information retrieval , 1993 .

[13]  Alex Pentland,et al.  Photobook: tools for content-based manipulation of image databases , 1994, Electronic Imaging.

[14]  Jiawei Han,et al.  Efficient and Effective Clustering Methods for Spatial Data Mining , 1994, VLDB.

[15]  Shih-Fu Chang,et al.  Transform features for texture classification and discrimination in large image databases , 1994, Proceedings of 1st International Conference on Image Processing.

[16]  R. Ng,et al.  Eecient and Eeective Clustering Methods for Spatial Data Mining , 1994 .

[17]  Rajiv Mehrotra,et al.  Similar-Shape Retrieval in Shape Data Management , 1995, Computer.

[18]  Borko Furht,et al.  Video and Image Processing in Multimedia Systems , 1995 .

[19]  Alex Pentland,et al.  Photobook: tools for content-based manipulation of image databases , 1994, Other Conferences.

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

[21]  Thomas P. Minka,et al.  An image database browser that learns from user interaction , 1996 .

[22]  Tian Zhang,et al.  BIRCH: an efficient data clustering method for very large databases , 1996, SIGMOD '96.

[23]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[24]  Josef Kittler,et al.  Robust and Efficient Shape Indexing through Curvature Scale Space , 1996, BMVC.

[25]  Amarnath Gupta,et al.  Virage image search engine: an open framework for image management , 1996, Electronic Imaging.

[26]  Tom Minka,et al.  Interactive learning with a "Society of Models" , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

[28]  Rosalind W. Picard.  A Society of Models for Video and Image Libraries , 1996, IBM Syst. J..

[29]  Andrew Calway,et al.  Proceedings of the IEEE International Conference on Image Processing , 1996 .

[30]  Raj Jain,et al.  Algorithms and strategies for similarity retrieval , 1996 .

[31]  Rosalind W. Picard,et al.  Interactive Learning Using a "Society of Models" , 2017, CVPR 1996.

[32]  Aidong Zhang,et al.  A Fractal-Based Clustering Approach in Large Visual Database Systems , 1996 .

[33]  Aidong Zhang,et al.  Geographical image classification and retrieval , 1997, GIS '97.

[34]  Raj Acharya,et al.  Color clustering techniques for color-content-based image retrieval from image databases , 1997, Proceedings of IEEE International Conference on Multimedia Computing and Systems.

[35]  Aidong Zhang,et al.  Approach to clustering large visual databases using wavelet transform , 1997, Electronic Imaging.

[36]  Ramin Zabih,et al.  Comparing images using color coherence vectors , 1997, MULTIMEDIA '96.

[37]  Jiong Yang,et al.  STING: A Statistical Information Grid Approach to Spatial Data Mining , 1997, VLDB.

[38]  Shih-Fu Chang,et al.  VisualSEEk: a fully automated content-based image query system , 1997, MULTIMEDIA '96.

[39]  Aidong Zhang,et al.  NeuroMerge: an approach for merging heterogeneous features in content-based image retrieval systems , 1998, Proceedings International Workshop on Multi-Media Database Management Systems (Cat. No.98TB100249).

[40]  Josef Kittler,et al.  Efficient and Robust Retrieval by Shape Content through Curvature Scale Space , 1998, Image Databases and Multi-Media Search.

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

[42]  David G. Lowe,et al.  Perceptual Organization and Visual Recognition , 2012 .