Abstract
Histogram feature representation is important in many classification applications for characterization of the statistical distribution of different pattern attributes, such as the color and edge orientation distribution in images. While the construction of these feature representations is simple, this very simplicity may compromise the classification accuracy in those cases where the original histogram does not provide adequate discriminative information for making a reliable classification. In view of this, we propose an optimization approach based on evolutionary computation (Back, Evolutionary algorithms in theory and practice, Oxford University Press, New York, 1996; Fogel, Evolutionary computation: toward a new philosophy of machine intelligence, 2nd edn. IEEE, Piscataway, NJ 1998) to identify a suitable transformation on the histogram feature representation, such that the resulting classification performance based on these features is maximally improved while the original simplicity of the representation is retained. To facilitate this optimization process, we propose a hierarchical classifier structure to demarcate the set of categories in such a way that the pair of category subsets with the highest level of dissimilarities is identified at each stage for partition. In this way, the evolutionary search process for the required transformation can be considerably simplified due to the reduced level of complexities in classification for two widely separated category subsets. The proposed approach is applied to two problems in multimedia data classification, namely the categorization of 3D computer graphics models and image classification in the JPEG compressed domain. Experimental results indicate that the evolutionary optimization approach, facilitated by the hierarchical classification process, is capable of significantly improving the classification performance for both applications based on the transformed histogram representations.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Ankerst M, Kastenmuller G, Kriegel HP, Seidl T (1999) 3D shape histograms for similarity search and classification in spatial databases. Proc. 6th Int. Symposium on Large Spatial Databases (SSD’99), Hong Kong, China, pp 207–226
Ankerst M, Kastenmuller G, Kriegel HP, Seidl T (1999) Nearest neighbor classification in 3D protein databases. Proc. 7th intl. conf. on intelligent systems for molecular biology, Heidelberg, Germany, pp. 34–43
Back T (1996) Evolutionary algorithms in theory and practice. Oxford University Press, New York
Bailey A, Harris CJ (1999) Using hierarchical classification to exploit context in pattern classification for information fusion, Proc. of proceedings of 2nd intl. conf. on information fusion, 1196–1203
Basri R, Weinshall D (1996) Distance medtric between 3D models and 2D images for recognition and classification. IEEE Trans Pattern Anal Mach Intell 18(4):465–470
Fogel DB (1998) Evolutionary computation: toward a new philosophy of machine intelligence, 2nd edn. IEEE, Piscataway, NJ
Funkhouser T, Min P, Kazhdan M, Chen J, Halderman A, Dobkin D (2003) A search engine for 3D models. ACM Trans Graph 21(3):83–105
Furht B (1995) A survey of multimedia compression techniques and standards. Real-Time Imaging 1:49–67
Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading, MA
Hilaga M, Shinagawa Y, Kohmura T, Kunii T (2001) Topology matching for fully automatic similarity estimation of 3D shapes. Proc. ACM SIGGRAPH ’01, Log Angeles, USA, pp. 203–212, Aug
Horn BKP (1984) Extended gaussian images. Proc IEEE 72(12):1671–1686
Ip HHS, Wong WYF (2002) 3D head models retrieval based on hierarchical facial region similarity. 15th Vision Interface, Calgary, Canada, pp 314–319, May
Kim B, Landgrebe DA (1991) Hierarchical classifier design in high dimensional numerous class cases. IEEE Trans Geosci Remote Sens 29(4):518–528
Landeweerd G, Timmers T, Gelsema E, Bins M, Halic M (1983) Binary tree versus single level tree classification of white blood cells. Pattern Recogn 16(6):571–577
Lau RWH, Wong B (2002) Web-based 3D geometry model retrieval. World Wide Web: Internet and Web Information Systems 5:193–206
Lay JA, Guan L (1999) Image retrieval based on energy histograms of the low frequency DCT coefficients. In: Proc IEEE int conf on acoustics, Speeck and Singnal Proc, pp 3009–3012
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
MPI Faces Database (2004) Max Planck Institute for Biological Cybernetics, available online at http://faces.kyb.tuebingen.mpg.de/
Paquet E, Rioux M, Murching A, Naveen T, Tabatabai A (2000) Description of shape information for 2-D and 3-D objects. Signal Process Image Commun 16:103–122
Pennebaker WB, Mitchell JL (1993) JPEG still image compression standard. Van Nostrand Reinhold, New York
Rounds E (1980) A combined non-parametric aproach to feature selection and binary decision tree design. Pattern Recogn 12(5):313–317
Safavian SR, Landgrebe D (1991) A survey of decision tree classifier methodology. IEEE Trans Syst Man Cybern 21(3):660–674
Vranic DV, Saupe D, Richter J (2001) Tools for 3D-object retrieval: karhunen-loeve transform and spherical harmonics. Proc. IEEE 2001 workshop multimedia signal processing, Cannes, France, pp. 293–298, Oct
Wu C, Landgrebe D, Swain P (1975) The decision tree approach to classification. Technical Report RE-EE 75-17, School Elec. Eng., Purdue University, Lafayette, IN
Yoon Y, Lee C, Lee GG (2004) Systematic construction of hierarchical classificatier in SVM-based text categorization, accepted for Proc. 1st intl. joint conf. on natural language processing, Sanya City, Hainan, China
You KC, Fu KS (1976) An approach to the design of a linear binary tree classifier. Proc. 3rd symp. machine processing of remotely sensed data, Purdue University
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wong, HS., Cheung, K.K.T., Chiu, CI. et al. Hierarchical multi-classifier system design based on evolutionary computation technique. Multimed Tools Appl 33, 91–108 (2007). https://doi.org/10.1007/s11042-006-0098-z
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-006-0098-z