An Impact of PCA-Mixture Models and Different Similarity Distance Measure Techniques to Identify Latent Image Features for Object Categorization

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


In the current image retrieval systems, there exists a problem of defining and identifying efficient features in order to successfully bridge the gap between low level and high level semantics. In this regard, we propose an approach of efficiently extracting semantic features by combination of EM algorithm and PCA techniques, and thereby exploring PCA-Mixture Model with various similarity techniques for image retrieval system. Firstly, Expectation Maximization (EM) algorithm is applied to learn mixture of eigen values to obtain optimized maximum likelihood clusters. secondly, Principal Component Analysis (PCA) is applied for different mixtures in order to extract efficient features. Further classification is performed using five different distance metrics. Our proposed method reported state-of-the-art classification rate with lesser features and achieved promising results in classifying Caltech-101 object categories compared with other baseline methods performed on the same dataset.


Image Retrieval latent variable EM algorithm PCA Similar distance metrics Semantic gap 


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© 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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