A Flexible Image Database System for Content-Based Retrieval

There is a growing need for the ability to query image databases based on similarity of image content rather than strict keyword search. As distance computations can be expensive, there is a need for indexing systems and algorithms that can eliminate candidate images without performing distance calculations. As user needs may change from session to session, there is also a need for run-time creation of distance measures. In this paper, we present FIDS, “flexible image database system.” FIDS allows the user to query the database based on complex combinations of dozens of predefined distance measures. Using an indexing scheme and algorithms based on the triangle inequality, FIDS can often return matches to the query image without directly comparing the query image to more than a small percentage of the database. This paper describes the technical contributions of the FIDS approach to content-based image retrieval.

[1]  Walter A. Burkhard,et al.  Some approaches to best-match file searching , 1973, Commun. ACM.

[2]  Linda G. Shapiro,et al.  Efficient image retrieval with multiple distance measures , 1997, Electronic Imaging.

[3]  Robert M. Haralick,et al.  Structural Descriptions and Inexact Matching , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Toshikazu Kato,et al.  Query by Visual Example - Content based Image Retrieval , 1992, EDBT.

[5]  Anil K. Jain,et al.  Image retrieval using color and shape , 1996, Pattern Recognit..

[6]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[7]  M. S. Costa,et al.  Scene analysis using appearance-based models and relational indexing , 1995, Proceedings of International Symposium on Computer Vision - ISCV.

[8]  Ramesh C. Jain,et al.  Similarity measures for image databases , 1995, Electronic Imaging.

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

[10]  Shi-Kuo Chang,et al.  Iconic Indexing by 2-D Strings , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Alberto Del Bimbo,et al.  Visual image retrieval by elastic deformation of object sketches , 1994, Proceedings of 1994 IEEE Symposium on Visual Languages.

[12]  S. Chatterjee,et al.  Similarity measures for image databases , 1995, Proceedings of 1995 IEEE International Conference on Fuzzy Systems..

[13]  Ricardo A. Baeza-Yates,et al.  Proximity Matching Using Fixed-Queries Trees , 1994, CPM.

[14]  William I. Grosky,et al.  Industrial part recognition using a component-index , 1990, Image Vis. Comput..

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

[16]  Stan Sclaroff,et al.  Object recognition and categorization using modal matching , 1994, Proceedings of 1994 IEEE 2nd CAD-Based Vision Workshop.

[17]  Ronald Fagin,et al.  Fuzzy queries in multimedia database systems , 1998, PODS '98.

[18]  Toshikazu Kato,et al.  A sketch retrieval method for full color image database-query by visual example , 1992, [1992] Proceedings. 11th IAPR International Conference on Pattern Recognition.

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

[20]  Hayit Greenspan,et al.  Finding Pictures of Objects in Large Collections of Images , 1996, Object Representation in Computer Vision.

[21]  Christos Faloutsos,et al.  QBIC project: querying images by content, using color, texture, and shape , 1993, Electronic Imaging.

[22]  Alberto Del Bimbo,et al.  3-D visual query language for image databases , 1992, J. Vis. Lang. Comput..

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

[24]  Serge J. Belongie,et al.  Region-based image querying , 1997, 1997 Proceedings IEEE Workshop on Content-Based Access of Image and Video Libraries.

[25]  Jeffrey K. Uhlmann,et al.  Satisfying General Proximity/Similarity Queries with Metric Trees , 1991, Inf. Process. Lett..

[26]  Alberto Del Bimbo,et al.  Sequence retrieval by contents through spatio temporal indexing , 1993, Proceedings 1993 IEEE Symposium on Visual Languages.

[27]  William I. Grosky,et al.  Index-based object recognition in pictorial data management , 1990, Comput. Vis. Graph. Image Process..

[28]  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.

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

[30]  Patrick M. Kelly,et al.  CANDID: comparison algorithm for navigating digital image databases , 1994, Seventh International Working Conference on Scientific and Statistical Database Management.

[31]  James C. French,et al.  Using the triangle inequality to reduce the number of comparisons required for similarity-based retrieval , 1996, Electronic Imaging.

[32]  Li WangDong-Chen He,et al.  Texture classification using texture spectrum , 1990, Pattern Recognit..

[33]  David Salesin,et al.  Fast multiresolution image querying , 1995, SIGGRAPH.

[34]  Anil K. Jain,et al.  Texture Analysis , 2018, Handbook of Image Processing and Computer Vision.