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An Exploration of Mixture Models to Maximize between Class Scatter for Object Classification in Large Image Datasets

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Advances in Signal Processing and Intelligent Recognition Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 264))

Abstract

This paper presents a method for determining the significant features of an image within a maximum likelihood framework by remarkably reducing the semantic gap between high level and low level features. With this concern, we propose a FLD-Mixture Models and analyzed the effect of different distance metrics for Image Retrieval System. In this method, first Expectation Maximization (EM) algorithm method is applied to learn mixture of Gaussian distributions to obtain best possible maximum likelihood clusters. Gaussian Mixture Models is used for clustering data in unsupervised context. Further, Fisher’s Linear Discriminant Analysis( FLDA) is applied for K = 4 mixtures to preserve useful discriminatory information in reduced feature space. Finally, six different distance measures are used for classification purpose to obtain an average classification rate. We examined our proposed model on Caltech-101, Caltech-256 & Corel-1k datasets and achieved state-of-the-art classification rates compared to several well known benchmarking techniques on the same datasets.

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Author information

Authors and Affiliations

  1. Department of ECE, Sri Jagadguru Balagangadhara Institute of Technology, Bangalore, India

    K. Mahantesh

  2. Department of MCA, Sri Jayachamarajendra College of Engineering, Mysore, India

    V. N. Manjunath Aradhya

  3. Department of CSE, HKBK College of Engineering, Bangalore, India

    C. Naveena

Authors
  1. K. Mahantesh
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  2. V. N. Manjunath Aradhya
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  3. C. Naveena
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Corresponding author

Correspondence to K. Mahantesh .

Editor information

Editors and Affiliations

  1. Technopark Campus, Indian Institute of Information Technology and Management – Kerala (IIITM-K), Trivandrum, Kerala, India

    Sabu M. Thampi

  2. Center for Computing Research, National Polytechnic Institute, Mexico City, Mexico

    Alexander Gelbukh

  3. Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur, India

    Jayanta Mukhopadhyay

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© 2014 Springer International Publishing Switzerland

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Mahantesh, K., Manjunath Aradhya, V.N., Naveena, C. (2014). An Exploration of Mixture Models to Maximize between Class Scatter for Object Classification in Large Image Datasets. In: Thampi, S., Gelbukh, A., Mukhopadhyay, J. (eds) Advances in Signal Processing and Intelligent Recognition Systems. Advances in Intelligent Systems and Computing, vol 264. Springer, Cham. https://doi.org/10.1007/978-3-319-04960-1_40

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  • DOI: https://doi.org/10.1007/978-3-319-04960-1_40

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-04959-5

  • Online ISBN: 978-3-319-04960-1

  • eBook Packages: EngineeringEngineering (R0)

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