Abstract
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.
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Mahantesh, K., Manjunath Aradhya, V.N., Naveena, C. (2014). An Impact of PCA-Mixture Models and Different Similarity Distance Measure Techniques to Identify Latent Image Features for Object Categorization. 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_33
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DOI: https://doi.org/10.1007/978-3-319-04960-1_33
Publisher Name: Springer, Cham
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