Skip to main content
SpringerLink
Log in

A Selective Weighted Late Fusion for Visual Concept Recognition

  • Chapter
  • First Online:
Book cover Fusion in Computer Vision

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

We propose a novel multimodal approach to automatically predict the visual concepts of images through an effective fusion of visual and textual features. It relies on a Selective Weighted Late Fusion (SWLF) scheme which, in optimizing an overall Mean interpolated Average Precision (MiAP), learns to automatically select and weight the best features for each visual concept to be recognized. Experiments were conducted on the MIR Flickr image collection within the ImageCLEF Photo Annotation challenge. The results have brought to the fore the effectiveness of SWLF as it achieved a MiAP of 43.69 % in 2011 which ranked second out of the 79 submitted runs, and a MiAP of 43.67 % that ranked first out of the 80 submitted runs in 2012.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://www.flickr.com

  2. 2.

    http://www.facebook.com

  3. 3.

    http://www.flickr.com/

  4. 4.

    http://www.flickr.com/

References

  1. Salton G, McGill MJ (1986) Introduction to modern information retrieval. McGraw-Hill Inc, New York

    Google Scholar 

  2. 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 22:1349–1380

    Article  Google Scholar 

  3. Picard RW (2000) Affective computing. MIT press, Cambridge

    Google Scholar 

  4. Mojsilović A, Gomes J, Rogowitz B (2004) Semantic-friendly indexing and quering of images based on the extraction of the objective semantic cues. Int J Comput Vision 56:79–107

    Article  Google Scholar 

  5. Snelick R, Uludag U, Mink A, Indovina M, Jain A (2005) Large-scale evaluation of multimodal biometric authentication using state-of-the-art systems. IEEE Trans Pattern Anal Mach Intell 27:450–455

    Article  Google Scholar 

  6. Machajdik J, Hanbury A (2010) Affective image classification using features inspired by psychology and art theory. In: Proceedings of the international conference on Multimedia, ACM, pp 83–92

    Google Scholar 

  7. Liu N, Dellandréa E, Chen L, Zhu C, Zhang Y, Bichot CE, Bres S, Tellez B (2013) Multimodal recognition of visual concepts using histograms of textual concepts and selective weighted late fusion scheme. Comput Vis Image Underst 117:493–512

    Article  Google Scholar 

  8. Everingham M, Van Gool L, Williams CK, Winn J, Zisserman A (2010) The pascal visual object classes (voc) challenge. Int J Comput Vis 88:303–338

    Article  Google Scholar 

  9. Smeaton AF, Over P, Kraaij W (2006) Evaluation campaigns and trecvid. In: MIR ’06: Proceedings of the 8th ACM international workshop on multimedia, information retrieval, pp 321–330

    Google Scholar 

  10. Nowak S, Nagel K, Liebetrau J (2011) The clef 2011 photo annotation and concept-based retrieval tasks. In: CLEF workshop notebook paper

    Google Scholar 

  11. Guillaumin M, Verbeek JJ, Schmid C (2010) Multimodal semi-supervised learning for image classification. In: Proceedings of CVPR, pp 902–909

    Google Scholar 

  12. Snoek CGM, Worring M, Smeulders AWM (2005) Early versus late fusion in semantic video analysis. In: Proceedings of the 13th annual ACM international conference on multimedia, pp 399–402

    Google Scholar 

  13. Ah-Pine J, Bressan M, Clinchant S, Csurka G, Hoppenot Y, Renders JM (2009) Crossing textual and visual content in different application scenarios. Multimedia Tools Appl 42:31–56

    Article  Google Scholar 

  14. Snoek CGM, Worring M, Geusebroek JM, Koelma DC, Seinstra FJ (2004) The mediamill trecvid 2004 semantic video search engine. In: Proceedings of the TRECVID workshop

    Google Scholar 

  15. Westerveld T, Vries APD, van Ballegooij A, de Jong F, Hiemstra D (2003) A probabilistic multimedia retrieval model and its evaluation. EURASIP J Appl Signal Process 2003:186–198

    Article  MATH  Google Scholar 

  16. Noble WS et al (2004) Support vector machine applications in computational biology. In: Schoelkopf B, Tsuda K, Vert, J-P (eds) Kernel methods in computational biology. MIT Press, Cambridge, pp 71–92

    Google Scholar 

  17. Pinquier J, Karaman S, Letoupin L, Guyot P, Mégret R., Benois-Pineau J, Gaestel Y, Dartigues JF (2012) Strategies for multiple feature fusion with hierarchical hmm: application to activity recognition from wearable audiovisual sensors. In: Proceedings of 21st international conference on pattern recognition (ICPR), IEEE, pp 3192–3195

    Google Scholar 

  18. Binder A, Samek W, Kloft M, Müller C, Müller KR., Kawanabe M (2011) The joint submission of the tu berlin and fraunhofer first (tubfi) to the imageclef2011 photo annotation task. In: CLEF workshop notebook paper

    Google Scholar 

  19. Nagel K, Nowak S, Kühhirt U, Wolter K (2011) The Fraunhofer IDMT at ImageCLEF 2011 photo annotation task. In: Proceedings of CLEF (Notebook Papers/Labs/Workshop)

    Google Scholar 

  20. Csurka G, Dance C, Fan L, Willamowski J, Bray C (2004) Visual categorization with bags of keypoints. In: Workshop on statistical learning in computer vision, ECCV, vol 1, p 22

    Google Scholar 

  21. Quenot G, Benois-Pineau J, Mansencal B, Rossi E, Cord M, Precioso F, Gorisse D, Lambert P, Augereau B, Granjon L et al (2008) Rushes summarization by IRIM consortium: redundancy removal and multi-feature fusion. In: Proceedings of the 2nd ACM TRECVid video summarization workshop, ACM, pp 80–84

    Google Scholar 

  22. Wu Y, Chang EY, Chang KCC, Smith JR (2004) Optimal multimodal fusion for multimedia data analysis. In: Proceedings of the 12th annual ACM international conference on Multimedia, pp 572–579

    Google Scholar 

  23. Znaidia A, Borgne HL, Popescu A (2011) CEA list’s participation to visual concept detection task of ImageCLEF 2011. In: CLEF workshop notebook paper

    Google Scholar 

  24. Kittler J, Hatef M, Duin RPW, Matas J (1998) On combining classifiers. IEEE Trans Pattern Anal Mach Intell 20:226–239

    Article  Google Scholar 

  25. Ben Soltana W, Huang D, Ardabilian M, Chen L, Ben Amar C (2010) Comparison of 2D/3D features and their adaptive score level fusion for 3D face recognition. In: 3D data processing, visualization and transmission (3DPVT)

    Google Scholar 

  26. Pudil P, Novovičová J, Kittler J (1994) Floating search methods in feature selection. Pattern Recogn Lett 15:1119–1125

    Article  Google Scholar 

  27. Rokach L (2010) Ensemble-based classifiers. Artif Intell Rev 33:1–39

    Article  Google Scholar 

  28. Breiman L, Breiman L (1996) Bagging predictors. Mach Learn 24:123–140

    Google Scholar 

  29. Fergus R, Fei-Fei L, Perona P, Zisserman A (2005) Learning object categories from google’s image search. In: 10th IEEE international conference on computer vision ICCV, IEEE, vol 2, pp 1816–1823

    Google Scholar 

  30. Schroff F, Criminisi A, Zisserman A (2007) Harvesting image databases from the web. In: IEEE 11th international conference on computer vision, ICCV, pp 1–8

    Google Scholar 

  31. Wang G, Hoiem D, Forsyth DA (2009) Building text features for object image classification. In: Proceedings of CVPR, pp 1367–1374

    Google Scholar 

  32. Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recogn 29:51–59

    Google Scholar 

  33. Zhu C, Bichot CE, Chen L (2010) Multi-scale color local binary patterns for visual object classes recognition. In: Proceedings of ICPR, pp 3065–3068

    Google Scholar 

  34. Pujol A, Chen L (2007) Line segment based edge feature using hough transform. In: Proceedings of the 7th IASTED international conference on visualization, imaging and image processing, ACTA Press, pp 201–206

    Google Scholar 

  35. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60:91–110

    Google Scholar 

  36. Mikolajczyk K, Schmid C (2004) Scale and affine invariant interest point detectors. Int J Comput Vis 60:63–86

    Google Scholar 

  37. Lazebnik S, Schmid C, Ponce J (2006) Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: Proceedings of CVPR, vol 2, pp 2169–2178

    Google Scholar 

  38. Li FF, Perona P (2005) A Bayesian hierarchical model for learning natural scene categories. In: Proceedings of CVPR, vol 2, pp 524–531

    Google Scholar 

  39. Van de Sande KEA, Gevers T, Snoek CGM (2010) Evaluating color descriptors for object and scene recognition. IEEE Trans Pattern Anal Mach Intell 32:1582–1596

    Article  Google Scholar 

  40. Tola E, Lepetit V, Fua P (2010) Daisy: an efficient dense descriptor applied to wide-baseline stereo. IEEE Trans Pattern Anal Mach Intell 32:815–830

    Google Scholar 

  41. Zhu C, Bichot CE, Chen L (2011) Visual object recognition using daisy descriptor. In: Proceedings of ICME, pp 1–6

    Google Scholar 

  42. Dunker P, Nowak S, Begau A, Lanz C (2008) Content-based mood classification for photos and music: a generic multi-modal classification framework and evaluation approach. In: Proceedings of multimedia information retrieval, pp 97–104

    Google Scholar 

  43. Liu N, Dellandréa E, Tellez B, Chen L (2011) Evaluation of features and combination approaches for the classification of emotional semantics in images. In: International conference on computer vision, theory and applications (VISAPP)

    Google Scholar 

  44. Liu N, Dellandréa E, Tellez B, Chen L, Chen L (2011) Associating textual features with visual ones to improve affective image classification. In: Proceedings of ACII, vol 1, pp 195–204

    Google Scholar 

  45. Valdez P, Mehrabian A (1994) Effects of color on emotions. J Exp Psychol Gen 123:394–409

    Article  Google Scholar 

  46. Itten J, Van Haagen E (1973) The art of color: the subjective experience and objective rationale of color. Van Nostrand Reinhold, New York

    Google Scholar 

  47. Tamura H, Mori S, Yamawaki T (1978) Texture features corresponding to visual perception. IEEE Trans Syst Man Cybern 8(6):460–472

    Google Scholar 

  48. Haralick RM (1979) Statistical and structural approaches to texture. Proc IEEE 67:786–804

    Article  Google Scholar 

  49. Anstey NA (1966) Correlation techniques—a reivew. Can J Explor Geophys 2:55–82

    Google Scholar 

  50. van de Sande K. Colordescriptor software. http://www.colordescriptors.com

  51. Colombo C, Bimbo AD, Pala P (1999) Semantics in visual information retrieval. IEEE Multimedia 6:38–53

    Article  Google Scholar 

  52. Dellandréa E, Liu N, Chen L (2010) Classification of affective semantics in images based on discrete and dimensional models of emotions. In: International workshop on content-based multimedia indexing (CBMI), pp 99–104

    Google Scholar 

  53. Ke Y, Tang X, Jing F (2006) The design of high-level features for photo quality assessment. In: IEEE computer society conference on computer vision and pattern recognition, vol 1, pp 419–426

    Google Scholar 

  54. Datta R, Li J, Wang JZ (2005) Content-based image retrieval: approaches and trends of the new age. In: Proceedings on multimedia information retrieval, pp 253–262

    Google Scholar 

  55. Viola PA, Jones MJ (2001) Robust real-time face detection. In: Proceedings of CCV, vol 57, pp 137–154

    Google Scholar 

  56. Budanitsky A, Hirst G (2001) Semantic distance in wordnet: an experimental, application-oriented evaluation of five measures. In: Workshop on WordNet and other lexical resources, 2nd meeting of the North American chapter of the association for computational linguistics

    Google Scholar 

  57. Miller GA (1995) Wordnet: a lexical database for English. Commun ACM 38:39–41

    Article  Google Scholar 

  58. Huiskes MJ, Lew MS (2008) The MIR flickr retrieval evaluation. In: Proceedings on multimedia information retrieval, pp 39–43

    Google Scholar 

  59. Huiskes MJ, Thomee B, Lew MS (2010) New trends and ideas in visual concept detection: the MIR flickr retrieval evaluation initiative. In: MIR ’10: Proceedings of the 2010 ACM international conference on multimedia, information retrieval, pp 527–536

    Google Scholar 

  60. Vapnik VN (1995) The nature of statistical learning theory. Springer New York Inc., New York

    Book  MATH  Google Scholar 

  61. Zhang J, Marszaek M, Lazebnik S, Schmid C (2007) Local features and kernels for classification of texture and object categories: a comprehensive study. Int J Comput Vis 73:213–238

    Article  Google Scholar 

  62. Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2:1–27

    Article  Google Scholar 

  63. Escalante HJ, Montes M, Sucar E (2011) Multimodal indexing based on semantic cohesion for image retrieval. Inf Retrieval 15:1–32

    Article  Google Scholar 

  64. van Gemert JC, Veenman CJ, Smeulders AWM, Geusebroek JM (2010) Visual word ambiguity. IEEE Trans Pattern Anal Mach Intell 32:1271–1283

    Article  Google Scholar 

  65. Liu N, Zhang Y, Dellandréa E, Bres S, Chen L (2012) LIRIS-Imagine at ImageCLEF 2012 photo annotation task. In: CLEF workshop notebook paper

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Université de Lyon, CNRS, Ecole Centrale de Lyon, LIRIS, UMR5205, Lyon, 69134, France

    Ningning Liu, Emmanuel Dellandréa, Bruno Tellez & Liming Chen

Authors
  1. Ningning Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Emmanuel Dellandréa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bruno Tellez
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Liming Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ningning Liu .

Editor information

Editors and Affiliations

  1. University Politehnica of Bucharest, Romania

    Bogdan Ionescu

  2. University of Bordeaux, Talence, France

    Jenny Benois-Pineau

  3. Queen Mary University of London, London, United Kingdom

    Tomas Piatrik

  4. Lab. of Informatics of Grenoble, France

    Georges Quénot

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Liu, N., Dellandréa, E., Tellez, B., Chen, L. (2014). A Selective Weighted Late Fusion for Visual Concept Recognition. In: Ionescu, B., Benois-Pineau, J., Piatrik, T., Quénot, G. (eds) Fusion in Computer Vision. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-05696-8_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-05696-8_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-05695-1

  • Online ISBN: 978-3-319-05696-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation