Hierarchical classification using a frequency-based weighting and simple visual features

Pattern Recognition Letters

Volumn 29, Issue 15, 2008, Pages 2011-2017

  Zhou, Xin ^a   Depeursinge, Adrien ^a   Müller, Henning ^a,b  

^a GENEVA UNIVERSITY HOSPITALS   (Switzerland)

^b University of Applied Sciences   (Switzerland)

Author keywords

Automatic annotation; Hierarchical image classification; Image retrieval; Medical imaging

Indexed keywords

ASPECT RATIO; CLASSIFICATION (OF INFORMATION); CODES (SYMBOLS); ERROR ANALYSIS; IMAGE ANALYSIS; IMAGE ENHANCEMENT; IMAGE RETRIEVAL; INFORMATION RETRIEVAL; LEARNING ALGORITHMS; OPTIMIZATION; PRESSURE DROP; TAXONOMIES; TEXT PROCESSING;

AUTOMATIC ANNOTATION; CLASSIFICATION METHODS; FEATURE SPACES; HIERARCHICAL CLASSIFICATIONS; HIERARCHICAL IMAGE CLASSIFICATION; IMAGE ANNOTATION; INVERSE DOCUMENT FREQUENCY; K-NEAREST NEIGHBORS; LEARNING TECHNIQUES; LOCAL INFORMATION; MEDICAL IMAGING; POINT FEATURES; RETRIEVAL PERFORMANCE; SIMPLE TECHNOLOGY; TERM FREQUENCY; VISUAL FEATURES; VOTING SCHEMES; WEIGHTING SCHEMES;

INFORMATION RETRIEVAL SYSTEMS;

EID: 52049094110     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2008.04.004     Document Type: Article

References (26)

1. Antani S., Lee D., Long R., and Thoma G. Evaluation of shape similarity measurement methods for spine X-ray images. J. Visual Comm. Image Represent. 15 3 (2004) 285-303
2. Chapelle O., Vapnik V., Bousquet O., and Mukherjee S. Choosing multiple parameters for support vector machines. Mach. Learn. 46 1 (2002) 131-159
3. Datta R., Joshi D., Li J., and Wang J.Z. Image retrieval: Ideas, influences, and trends of the new age. ACM Comput. Surv. 40 2 (2008) 1-60
4. Deselaers, T., Müller, H., Deserno, T.M., 2008. Automatic medical image annotation in ImageCLEF 2007: Overview, results, and discussion. Pattern Recognition Lett. (Special Issue on Medical Image Annotation in ImageCLEF 2007), 29 (15), 1988-1995.
5. Everingham, M., Zisserman, A., Williams, C.K.I., van Gool, L., Allan, M., Bishop, C.M., Chapelle, O., Dalal, N., Deselaers, T., Dorko, G., Duffner, S., Eichhorn, J., Farquhar, J.D.R., Fritz, M., Garcia, C., Griffiths, T., Jurie, F., Keysers, D., Koskela, M., Laaksonen, J., Larlus, D., Leibe, B., Meng, H., Ney, H., Schiele, B., Schmid, C., Seemann, E., Shawe-Taylor, J., Storkey, A., Szedmak, S., Triggs, B., Ulusoy, I., Viitaniemi, V., Zhang, J., 2006. The 2005 pascal visual object classes challenge. In: Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Textual Entailment (PASCAL Workshop 05). Lecture Notes in Artificial Intelligence, vol. 3944, Southampton, UK, pp. 117-176.
6. Fergus, R., Perona, P., Zisserman, A., 2004. A visual category filter for google images. In: Proc. 8th European Conf. on Computer Vision (ECCV 2004), vol. 1, Prague, Czech Republic, pp. 242-256.
7. Gass, T., Geissbuhler, A., Müller, H., 2007. Learning a frequency-based weighting for medical image classification. In: Medical Imaging and Medical Informatics (MIMI) 2007, Beijing, China, pp. 137-147.
8. Kato, T., 1992. Database architecture for content-based image retrieval. In: Jamberdino, A.A., Niblack, W. (Eds.), Image Storage and Retrieval Systems. SPIE Proc., vol. 1662, San Jose, CA, pp. 112-123.
9. Lehmann, T.M., Schubert, H., Keysers, D., Kohnen, M., Wein, B.B., 2003. The IRMA code for unique classification of medical images. In: Huang, H.K., Ratib, O.M. (Eds.), Medical Imaging 2003: PACS and Integrated Medical Information Systems: Design and Evaluation. SPIE Proc., vol. 5033, San Diego, CA, USA, pp. 440-451.
10. Lehmann T.M., Güld M.O., Deselaers T., Keysers D., Schubert H., Spitzer K., Ney H., and Wein B.B. Automatic categorization of medical images for content-based retrieval and data mining. Comput. Med. Imaging Graphics 29 2-3 (2005) 143-155
11. Müller H., Michoux N., Bandon D., and Geissbuhler A. A review of content-based image retrieval systems in medicine - clinical benefits and future directions. Internat. J. Med. Inform. 73 (2004) 1-23
12. Müller, H., Rosset, A., Vallée, J.-P., Geissbuhler, A., 2004b. Comparing feature sets for content-based medical information retrieval. In: Proc. SPIE Internat. Conf. on Medical Imaging, SPIE, vol. 5371, San Diego, CA, USA, pp. 99-109.
13. Müller, H., Heuberger, J., Geissbuhler, A., 2005. Logo and text removal for medical image retrieval. In: Meinzer, H.-P., Handels, H., Horsch, A., Tolxdorff, T. (Eds.), Springer Informatik aktuell: Proc. Workshop Bildverarbeitung für die Medizin, Springer, Heidelberg, Germany, pp. 35-39.
14. Müller, H., Pitkanen, M., Zhou, X., Depeursinge, A., Iavindrasana, J., Geissbuhler, A., 2007. KnowARC: Enabling grid networks for the biomedical research community. Healthgrid 2007, Geneva, Switzerland, pp. 261-268.
15. Niblack, W., Barber, R., Equitz, W., Flickner, M.D., Glasman, E.H., Petkovic, D., Yanker, P., Faloutsos, C., Taubin, G., 1993. QBIC project: Querying images by content, using color, texture, and shape. In: Niblack, W. (Ed.), Storage and Retrieval for Image and Video Databases. SPIE Proc., vol. 1908, pp. 173-187.
16. Pinz A. Object categorization. Found. Trends Comput. Graph. Vis. 1 4 (2005) 255-353
17. Qiu, B., 2006. A refined SVM applied in medical image annotation. In: Evaluation of Multilingual and Multi-modal Information Retrieval, Seventh Workshop of the Cross-Language Evaluation Forum (CLEF 2006). Lecture Notes in Computer Science, vol. 4730, Springer, Alicante, Spain, pp. 690-693.
18. Rui Y., Huang T.S., and Chang S.-F. Image retrieval: Past, present and future. J. Visual Comm. Image Represent. 10 (1999) 39-62
19. Smeulders A.W.M., Worring M., Santini S., Gupta A., and Jain R. Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Mach. Intell. 22 12 (2000) 1349-1380
20. Squire, D.M., Müller, W., Müller, H., Raki, J., 1999. Content-based query of image databases, inspirations from text retrieval: Inverted files, frequency-based weights and relevance feedback. In: The 11th Scandinavian Conference on Image Analysis (SCIA'99), Kangerlussuaq, Greenland, pp. 143-149.
21. Squire D.M., Müller W., Müller H., and Pun T. Content-based query of image databases: Inspirations from text retrieval. In: Ersboll B.K., and Johansen P. (Eds). Pattern Recognition Lett. 21 13-14 (2000) 1193-1198
22. Swain M.J., and Ballard D.H. Color indexing. Internat. J. Comput. Vis. 7 1 (1991) 11-32
23. Tagare H.D., Jaffe C., and Duncan J. Medical image databases: A content-based retrieval approach. J. Amer. Med. Inform. Assoc. 4 3 (1997) 184-198
24. Tommasi, T., Orabona, F., Caputo, B., 2007. CLEF2007 image annotation task: An SVM-based cue integration approach. In: Working Notes of the 2007 CLEF Workshop, Budapest, Hungary.
25. Vannier, M.W., Staab, E.V., Clarke, L.C., 2002. Medical image archives - present and future. In: Lemke, H.U., Vannier, M.W., Inamura, K., Farman, A.G., Reiber, J.H.C. (Eds.), Proceedings of the International Conference on Computer-Assisted Radiology and Surgery (CARS 2002), Paris, France, pp. 565-576.
26. Winter, A., Nastar, C., 1999. Differential feature distribution maps for image segmentation and region queries in image databases. In: IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'99), Fort Collins, Colorado, USA, pp. 9-17.