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

  • K. Mahantesh
  • V. N. Manjunath Aradhya
  • C. Naveena
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 264)


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.


Image Retrieval System Maximum likelihood clusters EM algorithm FLD Similarity measures Gausssian Mixtures 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • K. Mahantesh
    • 1
  • V. N. Manjunath Aradhya
    • 2
  • C. Naveena
    • 3
  1. 1.Department of ECESri Jagadguru Balagangadhara Institute of TechnologyBangaloreIndia
  2. 2.Department of MCASri Jayachamarajendra College of EngineeringMysoreIndia
  3. 3.Department of CSEHKBK College of EngineeringBangaloreIndia

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