Prior-based probabilistic latent semantic analysis for multimedia retrieval

Multimedia Tools and Applications

Volumn 77, Issue 13, 2018, Pages 16771-16793

  Fernandez Beltran, Ruben ^a   Pla, Filiberto ^a  

^a UNIVERSITAT JAUME I   (Spain)

Author keywords

Content based multimedia retrieval; Information reduction; Probabilistic latent semantic analysis; Topic models

Indexed keywords

COMPETITION; CONTENT BASED RETRIEVAL; STATISTICS; TITANIUM SHEET;

COMPUTATIONAL LEARNING; CONTENT-BASED MULTIMEDIA RETRIEVAL; INFORMATION REDUCTION; LDA (LATENT DIRICHLET ALLOCATION); MULTIMEDIA APPLICATIONS; MULTIMEDIA COLLECTIONS; PROBABILISTIC LATENT SEMANTIC ANALYSIS; TOPIC MODEL;

SEMANTICS;

EID: 85030569750     PISSN: 13807501     EISSN: 15737721     Source Type: Journal    
DOI: 10.1007/s11042-017-5247-z     Document Type: Article

References (40)

1. Apt C, Damerau F, Weiss SM (1994) Automated learning of decision rules for text categorization. ACM Trans Inf Syst 12:233
2. Blei DM (2012) Probabilistic topic models. Commun ACM 55(4):77–84
3. Blei DM, Lafferty JD (2006) Dynamic topic models. In: ACM International conference on machine learning
4. Blei D, Ng A, Jordan M (2003) Latent dirichlet allocation. J Mach Learn Res 3(4-5):993–1022
5. Bosch A, Zisserman A, Muñoz X (2006) Scene classification via plsa. In: European conference on computer vision, pp 517–530
6. Chang J, Gerrish S, Wang C, Boyd-graber JL, Blei DM (2009) Reading tea leaves: how humans interpret topic models. In: Advances in neural information processing systems 22, pp 288–296
7. Chen M, Mao S, Liu Y (2014) Big data: a survey. Mobile Networks and Applications 19(2):171–209
8. Duygulu P, Barnard K, Freitas JFGd, Forsyth DA (2002) Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary. In: European Conference on Computer Vision, pp 97–112
9. Fahad A, Alshatri N, Tari Z, Alamri A, Khalil I, Zomaya AY, Foufou S, Bouras A (2014) A survey of clustering algorithms for big data: taxonomy and empirical analysis. IEEE Transactions on Emerging Topics in Computing 2(3):267–279
10. Feng D, Siu WC, Zhang HJ (2010) Multimedia information retrieval and management: technological fundamentals and applications. Springer publishing company inc, Berlin
11. Fernandez-Beltran R, Pla F (2015) Incremental probabilistic latent semantic analysis for video retrieval. Image Vis Comput 38:1–12
12. Fernandez-Beltran R, Pla F (2016) Latent topics-based relevance feedback for video retrieval. Pattern Recogn 51:72–84
13. Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J Am Stat Assoc 32(200):675–701
14. Hofmann T (2001) Unsupervised learning by probabilistic latent semantic analysis. Mach Learn 42(1-2):177–196
15. Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6(2):65–70
16. Hu P, Liu W, Jiang W, Yang Z (2014) Latent topic model for audio retrieval. Pattern Recogn 47(3):1138–1143
17. Jain AK (2010) Data clustering: 50 years beyond k-means. Pattern Recogn Lett 31(8):651–666
18. Jiang YG, Ye G, Chang SF, Ellis D, Loui AC (2011) Consumer video understanding: a benchmark database and an evaluation of human and machine performance. In: ACM International conference on multimedia retrieval
19. Kakkonen T, Myller N, Sutinen E, Timonen J (2008) Comparison of dimension reduction methods for automated essay grading. Educ Technol Soc 11 (3):275–288
20. Lew MS, Sebe N, Djeraba C, Jain R (2006) Content-based multimedia information retrieval: State of the art and challenges. ACM Trans Multimed Comput Commun Appl 2(1):1–19
21. Li AQ, Ahmed A, Ravi S, Smola AJ (2014) Reducing the sampling complexity of topic models. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, pp 891–900
22. Lienhart R, Romberg S, Hörster E (2009) Multilayer plsa for multimodal image retrieval. In: ACM International conference on multimedia retrieval
23. Lowe DG (1999) Object recognition from local scale-invariant features. In: Proceedings of the seventh IEEE international conference on computer vision. 10.1109/ICCV.1999.790410, vol 2, pp 1150–1157
24. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110. 10.1023/B:VISI.0000029664.99615.94
25. Manning CD, Raghavan P, Schütze H (2008) Introduction to information retrieval. Cambridge University Press, Cambridge
26. Mikolajczyk K, Schmid C (2004) Scale &; affine invariant interest point detectors. Int J Comput Vis 60(1):63–86. 10.1023/B:VISI.0000027790.02288.f2
27. Monay F, Gatica-Perez D (2007) Modeling semantic aspects for cross-media image indexing. IEEE Trans Pattern Anal Mach Intell 29(10):1802–1817
28. Philip Chen CL, Zhang CY (2014) Data-intensive applications, challenges, techniques and technologies: a survey on big data. Inf Sci 275:314–347
29. Rui YRY, Huang TS, Mehrotra S, Ortega M (1997) A relevance feedback architecture for content-based multimedia information retrieval systems
30. Saxena A, Prasad M, Gupta A, Bharill N, Patel OP, Tiwari A, Er MJ, Ding W, Lin CT (2017) A review of clustering techniques and developments. Neurocomputing. 10.1016/j.neucom.2017.06.053
31. Sivic J, Zisserman A (2003) Video Google: a text retrieval approach to object matching in videos. In: IEEE International conference on computer vision, vol 2, pp 1470–1477
32. Smeulders AWM, 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(12):1349–1380
33. Sontag D, Roy D (2011) Complexity of inference in latent dirichlet allocation. In: Shawe-Taylor J, Zemel RS, Bartlett PL, Pereira F, Weinberger KQ (eds) Advances in neural information processing systems 24. Curran Associates Inc, Red Hook, pp 1008–1016
34. Than K, Ho TB (2012) Fully sparse topic models. In: European conference on machine learning, pp 490–505
35. Wilcoxon F (1945) Individual comparisons by ranking methods. Biom Bull 1 (6):80–83
36. Yi X, Allan J (2009) A comparative study of utilizing topic models for information retrieval. In: European conference on IR research on advances in information retrieval
37. Yoshitaka A, Ichikawa T (1999) A survey on content-based retrieval for multimedia databases. IEEE Trans Knowl Data Eng 11(1):81–93
38. Zeng J, Leng B, Xiong Z (2015) 3-d object retrieval using topic model. Multimedia Tools and Applications 74:7859–7881
39. Zhai C (2008) Statistical language models for information retrieval a critical review. Found Trends Inf Retr 2(3):137–213
40. Zhou X, Hu X, Zhang X (2007) Topic signature language models for ad hoc retrieval. IEEE Trans Knowl Data Eng 19(9):1276–1287