An Impact of Gaussian Mixtures in Image Retrieval System

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Author(s): K. Mahantesh (SJB Institute of Technology, India)and Manjunath Aradhya V N (Sri Jayachamarajendra College of Engineering, India)
Copyright: 2016
Pages: 30
Source title: Handbook of Research on Advanced Hybrid Intelligent Techniques and Applications
Source Author(s)/Editor(s): Siddhartha Bhattacharyya (RCC Institute of Information Technology, India), Pinaki Banerjee (Goldstone Infratech Limited, India), Dipankar Majumdar (RCC Institute of Information Technology, India)and Paramartha Dutta (Visva-Bharati University, India)
DOI: 10.4018/978-1-4666-9474-3.ch002

Keywords: Artificial Intelligence / Computer Science & IT / Information Science Reference

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Abstract

The difficulty of searching for patterns in data is still exploratory and, ever increasing image datasets with high intra-class variations has created a large scope for generalizing image classification problems. This chapter initiates the inclusivity of discrete latent variables leading to mixture of Gaussians capturing multimodal distributions from segmented regions. Further, these mixtures are analyzed in maximum likelihood framework to extract discriminatory features in compact and de-correlated feature space. Conversely, it is less evident in literature that combining these features with diverse distance measure techniques and neural network classifiers improves the classification performance. In this chapter, we study, explore and demonstrate the idea of subspace mixture models as hybrid intelligent technique for image retrieval systems.

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An Impact of Gaussian Mixtures in Image Retrieval System

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