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Cited by 762

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Image classification for content-based indexing

Vailaya, A., Figueiredo, M.A.T., Jain, A.K., Hong-Jiang Zhang·2001·IEEE Transactions on Image Processing
DOI 10.1109/83.892448
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Abstract

Grouping images into (semantically) meaningful categories using low-level visual features is a challenging and important problem in content-based image retrieval. Using binary Bayesian classifiers, we attempt to capture high-level concepts from low-level image features under the constraint that the test image does belong to one of the classes. Specifically, we consider the hierarchical classification of vacation images; at the highest level, images are classified as indoor or outdoor; outdoor images are further classified as city or landscape; finally, a subset of landscape images is classified into sunset, forest, and mountain classes. We demonstrate that a small vector quantizer (whose optimal size is selected using a modified MDL criterion) can be used to model the class-conditional densities of the features, required by the Bayesian methodology. The classifiers have been designed and evaluated on a database of 6931 vacation photographs. Our system achieved a classification accuracy of 90.5% for indoor/outdoor, 95.3% for city/landscape, 96.6% for sunset/forest and mountain, and 96% for forest/mountain classification problems. We further develop a learning method to incrementally train the classifiers as additional data become available. We also show preliminary results for feature reduction using clustering techniques. Our goal is to combine multiple two-class classifiers into a single hierarchical classifier.

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Citations

Cites

Research works printed but not linked: 44

  1. 10.4324/9781315512372-8 · DOI
  2. Biederman, 1988, Aspects and extensions of a theory of human image un-derstanding, 370
  3. 10.1109/5.237540 · doi:10.1109/5.237540
  4. Duda, 1973, Pattern Classification and Scene Anal-ysis.
  5. 10.1007/BF00962238 · DOI · Paper page
  6. 10.1109/ICPR.2000.906023 · DOI
  7. Furht, 1998, Content-based image indexing and retrieval
  8. 10.1109/MASSP.1984.1162229 · doi:10.1109/massp.1984.1162229
  9. 10.1109/SEQUEN.1997.666914 · DOI
  10. 10.1117/12.263407 · DOI
  11. Jain, 1988, Algorithms for Clustering Data.
  12. 10.1109/34.824819 · DOI · Paper page
  13. 10.1109/34.574797 · DOI · Paper page
  14. Kohonen, LVQ_PAK: A program package for the correct application of learning vector quantization algorithms, 725
  15. 10.1007/3-540-48432-9_5 · doi:10.1007/3-540-48432-9_5
  16. 10.1117/12.227251 · doi:10.1117/12.227251
  17. 10.1109/ICIP.1997.647976 · DOI
  18. 10.1016/0031-3203(92)90099-5 · DOI · Paper page
  19. McLachlan, 1997, The EM Algorithm and Extensions
  20. Mehrotra, Supporting con-tent-based queries over images in MARS, 3
  21. 10.1016/S0031-3203(96)00113-6 · DOI · Paper page
  22. Mitchell, 1997, Machine Learning
  23. 10.1109/TC.1977.1674939 · DOI · Paper page
  24. Papathomas, Psychophysical studies of the performance of an image database retrieval system, 591
  25. Pass, Comparing images using color coherence vectors, 10.1145/244130.244148 · DOI · Paper page
  26. Pentland, Photobook: Content-based manipulation of image databases, 34
  27. 10.1007/BF01236575 · DOI · Paper page
  28. Pudil, 1994, Floating search methods in feature selection, Pattern Recognit. Lett., 15, 1119, 10.1016/0167-8655(94)90127-9 · DOI · Paper page
  29. 10.1017/CBO9780511812651 · DOI · Paper page
  30. Rissanen, 1989, Stochastic Complexity in Stastistical Inquiry.
  31. Rogowitz, Percep-tual image similarity experiments, 576
  32. 10.1109/76.718510 · DOI · Paper page
  33. Singer, 1999, A new parameter estimation method for Gaussian mixtures
  34. 10.1145/244130.244151 · DOI
  35. Stricker, Color indexing with weak spatial con-straints, 29
  36. 10.1007/BF00130487 · DOI · Paper page
  37. 10.1109/CAIVD.1998.646032 · DOI · Paper page
  38. Vailaya, A Bayesian framework for semantic classification of outdoor vacation images, 3656, 415, 10.1117/12.333861 · doi:10.1117/12.333861
  39. Vailaya, Content-based hierarchical classification of vacation images, 1, 7
  40. 10.1016/S0031-3203(98)00079-X · DOI · Paper page
  41. 10.1109/DCC.1997.581989 · DOI
  42. 10.1016/b978-155860651-7/50116-9 · doi:10.1016/b978-155860651-7/50116-9
  43. Zhang, A scheme for visual feature based image in-dexing, 36
  44. 10.1117/12.234800 · doi:10.1117/12.234800

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Metadata

Authors
Vailaya, A., Figueiredo, M. A. T., Jain, A. K., & Zhang, H.-J.
Venue
IEEE Transactions on Image Processing
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2001
Volume
10
Issue
1
Pages
117-130
ISSN
1057-7149 (ISSN-L)
DOI
10.1109/83.892448
OpenAlex
W2138826255
Cited by
762
References
2
FWCI
31.77
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)

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