Beyond nouns: Exploiting prepositions and comparative adjectives for learning visual classifiers

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5302 LNCS, Issue PART 1, 2008, Pages 16-29

  Gupta, Abhinav ^a   Davis, Larry S ^a  

^a University of Maryland   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; LABELED DATA; LARGE DATASET; OBJECT RECOGNITION; SEMANTICS;

BINARY RELATIONSHIPS; CORRESPONDENCE PROBLEMS; LANGUAGE CONSTRUCTS; LARGE DATASETS; LEARNING MODELS; LEARNING PROCESS; SEMANTIC OBJECTS; VISUAL FEATURE;

LEARNING SYSTEMS;

EID: 57149125139     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-88682-2_3     Document Type: Conference Paper

References (22)

1. Armitage, L., Enser, P.: Analysis of user need in image archives. Journal of Information Science (1997)
2. Barnard, K., Duygulu, P., Freitas, N., Forsyth, D., Blei, D., Jordan, M.I.: Matching words and pictures. Journal of Machine Learning Research, 1107-1135 (2003)
3. Carneiro, G., Chan, A.B., Moreno, P., Vasconcelos, N.: Supervised learning of semantic classes for image annotation and retrieval. IEEE PAMI (2007)
4. Carbonetto, P., Freitas, N., Barnard, K.: A statistical model for general contextual object recognition. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 350-362. Springer, Heidelberg (2004)
5. Duygulu, P., Barnard, K., Freitas, N., Forsyth, D.: Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2353, pp. 97-112. Springer, Heidelberg (2002)
6. Barnard, K., Forsyth, D.: Learning the semantics of words and pictures. In: ICCV, pp. 408-415 (2001)
7. Andrews, S., Tsochantaridis, I., Hoffman, T.: Support vector machines for multipleinstance learning. In: NIPS (2002)
8. Li, J., Wang, J.: Automatic linguistic indexing of pictures by statistical modeling approach. IEEE PAMI (2003)
9. Maron, O., Ratan, A.: Multiple-instance learning for natural scene classification. ICML (1998)
10. Lavrenko, V., Manmatha, R., Jeon, J.: A model for learning the semantics of pictures. In: NIPS (2003)
11. Feng, S., Manmatha, R., Lavrenko, V.: Multiple bernoulli relevance models for image and video annotation. In: CVPR (2004)
12. Mori, Y., Takahashi, H., Oka, R.: Image to word transformation based on dividing and vector quantizing images with words. MISRM (1999)
13. Brown, P., Pietra, S., Pietra, V., Mercer, R.: The mathematics of statistical machine translation: Parameter estimation. Computational Linguistics (1993)
14. Srikanth, M., Varner, J., Bowden, M., Moldovan, D.: Exploiting ontologies for automatic image annotation. SIGIR (2005)
15. Jin, R., Chai, J., Si, L.: Effective automatic image annotation via a coherent language model and active learning. Mutimedia (2004)
16. Brill, E.: A simple rule-based part of speech tagger. ACL (1992)
17. Brill, E.: Transformation-based error-driven learning and natural language processing. Computational Linguistics (1995)
18. Ferrari, V., Zisserman, A.: Learning visual attributes. In: NIPS (2007)
19. Barnard, K., Yanai, K., Johnson, M., Gabbur, P.: Cross modal disambiguation. Toward Category-Level Object Recognition (2006)
20. Barnard, K., Fan, Q.: Reducing correspondence ambiguity in loosely labeled training data. In: CVPR (2007)
21. Barnard, K., Johnson, M.: Word sense disambigutaion with pictures. AI (2005)
22. Barnard, K., Fan, Q., Swaminathan, R., Hoogs, A., Collins, R., Rondot, P., Kaufold, J.: Evaluation of localized semantics: data, methodology and experiments. Univ. of Arizona, TR-2005 (2005)