Toward a higher-level visual representation for content-based image retrieval

Multimedia Tools and Applications

Volumn 60, Issue 2, 2012, Pages 455-482

  El sayad, Ismail ^a   Martinet, Jean ^a   Urruty, Thierry ^a   Djeraba, Chabane ^a  

^a CNRS   (France)

Author keywords

Content based image retrieval; Gaussian mixture model; PLSA; Spatial weighting; SURF; Visual phrases; Visual words

Indexed keywords

IMAGE SEGMENTATION; QUERY PROCESSING;

CONTENT BASED IMAGE RETRIEVAL; GAUSSIAN MIXTURE MODEL; PLSA; SPATIAL WEIGHTING; SURF; VISUAL PHRASE; VISUAL WORD;

CONTENT BASED RETRIEVAL;

EID: 84887153565     PISSN: 13807501     EISSN: 15737721     Source Type: Journal    
DOI: 10.1007/s11042-010-0596-x     Document Type: Conference Paper

References (39)

1. Agrawal R, Imielinski T, Swami AN (1993) Mining association rules between sets of items in large databases. In: Buneman P, Jajodia S (eds) SIGMOD conference. ACM Press, pp 207-216
2. Baeza-Yates RA, Ribeiro-Neto BA (1999) Modern information retrieval. ACMPress /Addison-Wesley
3. Bay H, Ess A, Tuytelaars T, Gool LJV (2008) Speeded-up robust features (surf). Comput Vis image Underst 110(3):346-359
4. Belongie S, Malik J, Puzicha J (2002) Shape matching and object recognition using shape contexts. IEEE Trans Pattern Anal Mach Intell 24(4):509-522
5. Bilmes J (1997)Agentle tutorial of theEMalgorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models. Tech Rep TR-97-021, ICSI
6. Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 8(6):679-698
7. Carson C, Thomas M, Belongie S, Hellerstein JM, Malik J (1999) Blobworld: a system for regionbased image indexing and retrieval. In: HuijsmansDP, SmeuldersAWM(eds) VISUAL. Lecture notes in computer science, vol 1614. Springer, pp 509-516
8. Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the em algorithm. J R Stat Soc Ser B 39(1):1-38
9. Fei-Fei L, Fergus R, Perona P (2007) Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories. Comput Vis image Underst 106(1):59-70
10. Haddad M, Chevallet JP, Bruandet MF (2000) Relations between terms discovered by association rules. In: 4th European conference on principles and practices of knowledge discovery in databases PKDD'2000, workshop on machine learning and textual information access, Lyon France
11. Hammouda KM, Kamel MS (2004) Efficient phrase-based document indexing for web document clustering. IEEE Trans Knowl Data Eng 16(10):1279-1296
12. Hofmann T (2001) Unsupervised learning by probabilistic latent semantic analysis. Mach Learn 42(1-2):177-196
13. Hoíng NV, Gouet-Brunet V, Rukoz M, Manouvrier M (2010) Embedding spatial information into image content description for scene retrieval. Pattern Recognit 43(9):3013-3024
14. Jégou H, Douze M, Schmid C, Pérez P (2010) Aggregating local descriptors into a compact image representation. In: IEEE conference on computer vision & pattern recognition. http://lear.inrialpes.fr/pubs/2010/ JDSP10
15. Jeon J, Manmatha R (2004) Using maximum entropy for automatic image annotation. In: In Proc. CIVR, pp 24-32
16. Jing F, Li M, Zhang L, Jiang Zhang H, Zhang B (2003) Learning in region-based image retrieval. In: In Proceedings of the IEEE international symposium on circuits and systems. Springer, pp 206-215
17. Jurie F, Triggs B (2005) Creating efficient codebooks for visual recognition. In: ICCV, pp 604-610
18. Lienhart R, Romberg S, Hörster E (2009) Multilayer plsa for multimodal image retrieval. In: CIVR '09: Proceeding of the ACM international conference on image and video retrieval. ACM, New York, NY, USA, pp 1-8
19. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91-110
20. Martinet J, Satoh S (2007) A study of intra-modal association rules for visual modality representation. In: Content-based multimedia indexing, 2007. CBMI '07. International workshop on, pp 344-350. doi:10.1109/CBMI.2007.385432
21. Morand C, Benois-Pineau J, Domenger JP, Zepeda J, Kijak E, Guillemot C (2010) Scalable object-based video retrieval in hd video databases. Image Commun 25(6):450-465
22. Nistér D, Stewénius H (2006) Scalable recognition with a vocabulary tree. In: CVPR (2), pp 2161-2168
23. Quelhas P, Monay F, Odobez JM, Gatica-Perez D, Tuytelaars T, Gool LJV (2005) Modeling scenes with local descriptors and latent aspects. In: ICCV, pp 883-890
24. Quelhas P, Monay F, Odobez JM, Gatica-Perez D, Tuytelaars T (2007) A thousand words in a scene. IEEE Trans Pattern Anal Mach Intell 29(9):1575-1589
25. van Rijsbergen CJ (1979) Information retrieval, 2nd edn. Butterworths, London
26. Salton G, Buckley C (1988) Term-weighting approaches in automatic text retrieval. Inf Process Manag 24(5):513-523
27. Salton G, Wong A, Yang CS (1975) A vector space model for automatic indexing. Commun ACM 18(11):613-620
28. Sivic J, Zisserman A (2003) Video google: a text retrieval approach to object matching in videos. In: ICCV. IEEE Computer Society, pp 1470-1477
29. Smeulders AWM, Member S, Worring M, Santini S, Gupta A, Jain R (2000) Content-based image retrieval at the end of the early years. IEEE Trans Pattern Anal Mach Intell 22:1349-1380
30. Tirilly P, Claveau V, Gros P (2008) Language modeling for bag-of-visual words image categorization. In: CIVR, pp 249-258
31. Viola PA, Jones MJ (2001) Rapid object detection using a boosted cascade of simple features. In: CVPR (1), pp 511-518
32. Willamowski J, Arregui D, Csurka G, Dance CR, Fan L (2004) Categorizing nine visual classes using local appearance descriptors. In: In ICPR workshop on learning for adaptable visual systems
33. Witten IH, Moffat A, Bell TC (1999) Managing gigabytes: compressing and indexing documents and images, 2nd edn. Morgan Kaufmann
34. Yang J, Jiang YG, Hauptmann AG, Ngo CW(2007) Evaluating bag-of-visual-words representations in scene classification. In: Multimedia information retrieval, pp 197-206
35. Yang Y, Pedersen JO (1997) A comparative study on feature selection in text categorization. In: ICML, pp 412-420
36. Yuan J, Wu Y, Yang M (2007) Discovery of collocation patterns: from visual words to visual phrases. In: CVPR
37. Zhang J, Marszalek M, Lazebnik S, Schmid C (2007) Local features and kernels for classification of texture and object categories: a comprehensive study. Int J Comput Vis 73(2):213-238
38. Zheng QF, Gao W (2008) Constructing visual phrases for effective and efficient object-based image retrieval. TOMCCAP 5(1)
39. Zhou X, Zhuang X, Yan S, Chang SF, Hasegawa-Johnson M, Huang TS (2008) Sift-bag kernel for video event analysis. In: ACM Multimedia, pp 229-238