Skip to main content
SpringerLink
Log in

Improving image annotation via useful representative feature selection

  • Research Report
  • Published:
Cognitive Processing Aims and scope Submit manuscript

Abstract

This paper describes the automatic assignment of images into classes described by individual keywords provided with the Corel data set. Automatic image annotation technology aims to provide an efficient and effective searching environment for users to query their images more easily, but current image retrieval systems are still not very accurate when assigning images into a large number of keyword classes. Noisy features are the main problem, causing some keywords never to be assigned to their correct images. This paper focuses on improving image classification, first by selection of features to characterise each image, and then the selection of the most suitable feature vectors as training data. A Pixel Density filter (PDfilter) and Information Gain (IG) are proposed to perform these respective tasks. We filter out the noisy features so that groups of images can be represented by their most important values. The experiments use hue, saturation and value (HSV) colour feature space to categorise images according to one of 190 concrete keywords or subsets of these. The study shows that feature selection through the PDfilter and IG can improve the problem of spurious similarity.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Notes

  1. Images can be characterised by texture as well as colour. Texture is typically described by the wavelet transform (Daubechies 1992). However, the emphasis of this paper is on colour.

  2. A software review is given at: http://www.uottawa.ca/academic/cut/options/Nov_96/CorelCDs.htm.

  3. A software review is given at: http://www.gtpcc.org/gtpcc/corelgallery.htm.

  4. For more information about WordNet please visit http://www.wordnet.princeton.edu/.

  5. Available at: http://www.cs.washington.edu/research/imagedatabase/.

References

  • Baeza-Yates R, Ribeiro-Neto B (1999) Modern information retrieval. Addison Wesley, England

    Google Scholar 

  • Barnard K, Duygulu P, Forsyth D, de Freitas N, Blei DM, Jordan MI (2003) Matching words and pictures. J Mach Learn Res 3:1107–1135

    Article  Google Scholar 

  • Belew RK (2000) Finding out about: a cognitive perspective on search engine technology and the WWW. Cambridge University Press, Cambridge

    Google Scholar 

  • Bishop CM (2006) Pattern recognition and machine learning. Springer, New York

    Google Scholar 

  • Blei DM, Ng AY, Jordan MI (2003) Latent Dirichlet allocation. J Mach Learn Res 3:993–1022

    Article  Google Scholar 

  • Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. CRC Press, Boca Raton

    Google Scholar 

  • Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Mining Knowl Discov 2(2):121–167

    Article  Google Scholar 

  • Carneiro G, Chan AB, Moreno PJ, Vasconcelos N (2007) Supervised learning of semantic classes for image annotation and retrieval. IEEE Trans Pattern Anal Mach Intell 29(3):394–410

    Article  PubMed  Google Scholar 

  • Daubechies I (1992) Ten lectures on wavelets. Society for Industrial and Applied Mathematics, Philadelphia

    Google Scholar 

  • Del Bimbo A (1996) Image and video databases: visual browsing, querying and retrieval. J Vis Lang Comput 7(4):353–359

    Article  Google Scholar 

  • Del Bimbo A (1999) Visual information retrieval. Morgan Kaufmann, San Francisco

    Google Scholar 

  • Eakins JP, Graham ME (1999) Content-based image retrieval: a report of the JISC technology applications programme. The Joint Information Systems Committee (JISC). http://www.jisc.ac.uk/uploaded_documents/jtap-039.doc (26 January 2007)

  • Gonzalez RC, Woods RE, Eddins SL (2004) Digital image processing using MATLAB. Pearson Prentice-Hall, Upper Saddle River

    Google Scholar 

  • Grubinger M, Clough P, Müller H, Deselaers T (2006) The IAPR TC-12 Benchmark—a new evaluation resource for visual information systems. In: Proceedings of the International Workshop OntoImage’2006 Language Resources for Content-Based Image Retrieval, held in conjunction with LREC’06. Genoa, Italy, 22 May 2006, pp 13–23

  • Gupta A, Santini S, Jain R (1997) In search of information in visual media. Commun ACM 40(12):35–42

    Article  Google Scholar 

  • Howarth P, Rüger S (2004) Evaluation of texture features for content-based image retrieval. International Conference on Image and Video Retrieval (CIVR), Dublin, pp 326–334

    Google Scholar 

  • Idris F, Panchanathan S (1997) Review of image and video indexing techniques. J Vis Commun Image Represent 8(2):146–166

    Article  Google Scholar 

  • Jain AK, Duin RPW, Mao J (2000) Statistical pattern recognition: a review. IEEE Trans Pattern Anal Mach Intell 22(1):4–37

    Article  Google Scholar 

  • Jeon J, Manmatha R (2004) using maximum entropy for automatic image annotation. In: Proceedings of the International Conference on Image and Video Retrieval, Dublin, Ireland, July 21–23 2004: 24–32

  • Jin X, French JC (2003) Improving image retrieval effectiveness via multiple queries. In: Proceedings of the First ACM International Workshop on Multimedia Database, New Orleans, LA, USA, pp 86–93

  • Jörgensen C, Jaimes A, Benitez AB, Chang S (2001) A conceptual framework and research for classifying visual descriptors. J Am Soc Inf Sci 52(11):938–947 Special Issue on Image Access: Bridging Multiple Needs and Multiple Perspectives

    Article  Google Scholar 

  • Lai T (2000) CHROMA: a photographic image retrieval system. PhD Thesis. University of Sunderland, UK

  • Lavrenko V, Manmatha R, Jeon J (2003) A model for learning the semantics of pictures. In: Proceedings of the International Conference on Neural Information Processing Systems, Vancouver, Canada, 8–13 December 2003

  • Lew MS (2001) Principles of visual information retrieval. Springer, London

    Google Scholar 

  • Li J, Wang JZ (2003) Automatic linguistic indexing of pictures by a statistical modelling approach. IEEE Trans Pattern Anal Mach Intell 25(9):1075–1088

    Article  Google Scholar 

  • Long F, Zhang H, Feng DD (2003) Fundamentals of content-based image retrieval. In: Feng DD, Siu WC, Zhang H (eds) Multimedia information retrieval and management—technological fundamentals and applications. Springer, Germany

    Google Scholar 

  • Mandal MK, Idris F, Panchanathan S (1999) A critical evaluation of image and video indexing techniques in the compressed domain. Image Vis Comput 17(7):513–529

    Article  Google Scholar 

  • Manning CD, Schütze H (1999) Foundations of statistical natural language processing. MIT, London

    Google Scholar 

  • Mathias E, Conci A (1998) Comparing the influence of color spaces and metrics in content based image retrieval. In: Proceedings of the IEEE International Symposium on Computer Graphics, Image Processing, and Vision. Rio de Janeiro, Brazil, 20–23 October 1998, pp 371–378

  • Mitchell TM (1997) Machine learning. McGraw Hill, New York

    Google Scholar 

  • Müller H, Müller W, Marchand-Maillet S, Pun T, Squire DM (2003) A framework for benchmarking in CBIR. Multimedia Tools Appl 21(1):55–73

    Article  Google Scholar 

  • Oakes MP (1998) Statistics for corpus linguistics. Edinburgh University Press, Edinburgh

    Google Scholar 

  • Quinlan JR (1986) Induction of decision trees. Mach Learn 1(1):81–106

    Google Scholar 

  • Shanbehzadeh J, Moghadam AME, Mahmoudi F (2000) Image indexing and retrieval techniques: past, present, and next. In: Proceedings of SPIE, The International Society for Optical Engineering, 3972, pp 461–490

  • Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J. 27, July and October: 379–423 and 623–656

  • Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8):888–905

    Article  Google Scholar 

  • Smeaton AF, Kraaij W, OverP (2004) The TREC video retrieval evaluation (TRECVID): a case study and status report. In: Proceedings of the RIAO 2004 Conference. Avignon, France, 26–28 April 2004, pp 25–37

  • Swain MJ, Ballard DH (1991) Color indexing. Int J Comp Vis 7(1):11–32

    Article  Google Scholar 

  • Tsai C (2005) Automatically annotating images with keywords. PhD Thesis, University of Sunderland, UK

  • Tsai C, McGarry K, Tait J (2006) Qualitative evaluation of automatic assignment of keywords to images. Inf Process Manage 42(1):136–154

    Article  Google Scholar 

  • Vailaya A (2000) Semantic classification in image databases. PhD Thesis. Michigan State University, USA

  • van der Heijden F (1994) Image based measurement systems: object recognition and parameter estimation. Wiley, Chichester

    Google Scholar 

  • Wu JK, Kankanhalli MS, Lim J, Hong D (2000) Perspectives on content-based multimedia systems. Kluwer Academic Publishers, London

    Google Scholar 

  • Yang Y, Pedersen JO (1997) A comparative study on feature selection in text categorization. In: Proceedings of the Fourteenth International Conference on Machine Learning. 8–12 July 1997, pp 412–420

Download references

Author information

Authors and Affiliations

  1. Department of Computing, Engineering and Technology, University of Sunderland, Sunderland, SR6 0DD, UK

    Wei-Chao Lin & Michael Oakes

  2. Information Retrieval Facility, Eschenbachgasse 11/3 Stk., 1010, Vienna, Austria

    John Tait

  3. Department of Information Management, National Central University, Jhongli, 32001, Taiwan

    Chih-Fong Tsai

Authors
  1. Wei-Chao Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael Oakes
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. John Tait
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chih-Fong Tsai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wei-Chao Lin.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lin, WC., Oakes, M., Tait, J. et al. Improving image annotation via useful representative feature selection. Cogn Process 10, 233–242 (2009). https://doi.org/10.1007/s10339-008-0247-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10339-008-0247-6

Keywords

Search

Navigation