A user study to investigate semantically relevant contextual information of WWW images

International Journal of Human Computer Studies

Volumn 68, Issue 5, 2010, Pages 270-287

  Fauzi, Fariza ^a   Belkhatir, Mohammed ^b  

^a MONASH UNIVERSITY MALAYSIA   (Malaysia)

^b UNIVERSITÉ LYON 1   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ATOMIC UNITS; CONTENT-BASED; CONTEXTUAL INFORMATION; DATA SETS; DESCRIPTORS; DIFFERENT DISTRIBUTIONS; DOM TREE; IMAGE SEGMENTS; LIMITING FACTORS; SEGMENTATION ALGORITHMS; SEGMENTATION METHODS; SEMANTIC CONCEPT; SEMANTIC GAP; SEMANTIC INTERPRETATION; STATISTICAL PROPERTIES; TEXT-BASED INFORMATION; TEXTUAL INFORMATION; USER STUDY; WEB IMAGES; WEB-PAGE; WWW IMAGES;

ABSTRACTING; CHARACTERIZATION; CONCENTRATION (PROCESS); DATA PROCESSING; LOCATION; SEARCH ENGINES; SEMANTICS;

SEMANTIC WEB;

EID: 77949265458     PISSN: 10715819     EISSN: 10959300     Source Type: Journal    
DOI: 10.1016/j.ijhcs.2010.01.001     Document Type: Article

References (36)

1. Jaimes A., and Chang S.-F. A conceptual framework for indexing visual information at multiple levels. SPIE Internet Imaging 3964 (2000) 2000
2. Arasu A., and Garcia-Molina H. Extracting structured data from Web pages. Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data (2003), ACM Press, San Diego, California
3. Belkhatir, M., P. Mulhem, and Y. Chiaramella, 2005. A Full-Text Framework for the Image Retrieval Signal/Semantic Integration, in Database and Expert Systems Applications, pp. 113-123.
4. Berger V.W., and Zhou Y. Binomial distribution: estimating and testing parameters. In: Everitt B.S., and Howell D.C. (Eds). Encyclopedia of Statistics in Behavioural Science (2005), John Wiley & Sons, Ltd., Chichester 155-157
5. Bradshaw B. Semantic based image retrieval: a probabilistic approach. Proceedings of the Eighth ACM International Conference on Multimedia (2000), ACM Press, Marina del Rey, California, United States
6. Cai D., et al. Hierarchical clustering of WWW image search results using visual, textual and link information. Proceedings of the 12th Annual ACM International Conference on Multimedia (2004), ACM, New York, NY, USA
7. Cai, D., et al., 2003. VIPS: a vision-based page segmentation algorithm. Microsoft Technical Report (MSR-TR-2003-79).
8. Chen Z., et al. Web mining for web image retrieval. J. Am. Soc. Inf. Sci. Technol. 52 10 (2001) 831-839
9. Crescenzi, V., G. Mecca, and P. Merialdo, 2002. Automatic web information extraction in the ROADRUNNER System. In: Conceptual Modeling for New Information Systems Technologies, pp. 264-277.
10. Feng H., Shi R., and Chua T.-S. A bootstrapping framework for annotating and retrieving WWW images. Proceedings of the 12th Annual ACM International Conference on Multimedia (2004), ACM Press, NY, USA
11. Flickner M., et al. Query by image and video content: the QBIC system. Computer 28 9 (1995) 23-32
12. Frankel C., Swain M.J., and Athitsos V. WebSeer: An Image Search Engine for the World Wide Web (1996), University of Chicago
13. Gao B., et al. Web image clustering by consistent utilization of visual features and surrounding texts. Proceedings of the 13th Annual ACM International Conference on Multimedia (2005), ACM, Hilton, Singapore
14. Gevers T. Color-based retrieval. Principles of visual information retrieval (2001), Springer-Verlag p. 11-49
15. Gong Z., L.H.U., and Cheang C.W. Web image indexing by using associated texts. Knowl. Inf. Syst. 10 2 (2006) 243-264
16. He X., et al. Clustering and searching WWW images using link and page layout analysis. ACM Trans. Multimedia Comput. Commun. Appl. 3 2 (2007) 10
17. Hollink L., et al. Classification of user image descriptions. Int. J. Hum. Comput. Stud. 61 5 (2004) 601-626
18. http://image.google.com
19. Hua Z., et al. Semantic knowledge extraction and annotation for web images. Proceedings of the 13th Annual ACM International Conference on Multimedia (2005), ACM, Hilton, Singapore
20. Jeon J., Lavrenko V., and Manmatha R. Automatic image annotation and retrieval using cross-media relevance models. Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval (2003), ACM Press, Toronto, Canada
21. Li F.-F., Fergus R., and Perona P. Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. Comput. Vis. Image Underst. 106 1 (2007) 59-70
22. Li, J., et al., 2006. A broadcast model for Web image annotation. In: Advances in Multimedia Information Processing-PCM, pp. 245-251.
23. Liu J., et al. Image annotation via graph learning. Pattern Recognition 42 2 (2009) 218-228
24. Liu Y., et al. A survey of content-based image retrieval with high-level semantics. Pattern Recognition 40 1 (2007) 262-282
25. Lu Y., et al. A unified framework for semantics and feature based relevance feedback in image retrieval systems. Proceedings of the Eighth ACM International Conference on Multimedia (2000), ACM Press, Marina del Rey, CA, United States
26. Malhotra N.K. Marketing Research-An Applied Orientation. Third Edition (1999), Prentice Hall 763
27. Meghini C., Sebastiani F., and Straccia U. A model of multimedia information retrieval. J. ACM 48 5 (2001) 909-970
28. Rorissa A. User-generated descriptions of individual images versus labels of groups of images: a comparison using basic level theory. Inf. Process. Manage. 44 5 (2008) 1741-1753
29. Sclaroff S., Cascia M.L., and Sethi S. Unifying textual and visual cues for content-based image retrieval on the World Wide Web. Comput. Vis. Image Underst. 75 1-2 (1999) 86-98
30. Shen H.T., Ooi B.C., and Tan K.-L. Giving meanings to WWW images. Proceedings of the Eighth ACM International Conference on Multimedia. (2000), ACM, Marina del Rey, CA, United States
31. Smeulders A.W.M., et al. Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Mach. Intell. 22 12 (2000) 1349-1380
32. Smith J.R., and Chang S.-f. Querying by color regions using VisualSEEk content-based visual query system. Intelligent multimedia information retrieval (1997), MIT Press p. 23-41
33. Tate M., and Alexander J. Teaching critical evaluation skills for World Wide Web resources. Comput. Libr. 16 10 (1996) 49-55
34. Wang C., Zhang L., and Zhang H.-J. Learning to reduce the semantic gap in web image retrieval and annotation. Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (2008), ACM, Singapore, Singapore
35. Wang X.-J., et al. Multi-model similarity propagation and its application for web image retrieval. Proceedings of the 12th Annual ACM International Conference on Multimedia (2004), ACM Press, NY, USA
36. Wang X.-J., et al. Iteratively clustering web images based on link and attribute reinforcements. Proceedings of the 13th Annual ACM International Conference on Multimedia (2005), ACM Press, Hilton, Singapore