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Dealing with Higher Dimensionality and Outliers in Content-Based Image Retrieval
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Author(s): Seikh Mazharul Islam (RCC Institute of Information Technology, India), Minakshi Banerjee (RCC Institute of Information Technology, India)and Siddhartha Bhattacharyya (RCC Institute of Information Technology, India)
Copyright: 2017
Pages: 26
Source title:
Intelligent Multidimensional Data Clustering and Analysis
Source Author(s)/Editor(s): Siddhartha Bhattacharyya (RCC Institute of Information Technology, India), Sourav De (Cooch Behar Government Engineering College, India), Indrajit Pan (RCC Institute of Information Technology, India)and Paramartha Dutta (Visva-Bharati University, India)
DOI: 10.4018/978-1-5225-1776-4.ch005
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Abstract
This chapter proposes a content based image retrieval method dealing with higher dimensional feature of images. The kernel principal component analysis (KPCA) is done on MPEG-7 Color Structure Descriptor (CSD) (64-bins) to compute low-dimensional nonlinear-subspace. Also the Partitioning Around Medoids (PAM) algorithm is used to squeeze search space again where the number of clusters are counted by optimum average silhouette width. To refine these clusters further, the outliers from query image's belonging cluster are excluded by Support Vector Clus-tering (SVC). Then One-Class Support Vector Machine (OCSVM) is used for the prediction of relevant images from query image's belonging cluster and the initial retrieval results based on the similarity measurement is feed to OCSVM for training. Images are ranked from the positively labeled images. This method gives more than 95% precision before recall reaches at 0.5 for conceptually meaningful query categories. Also comparative results are obtained from: 1) MPEG-7 CSD features directly and 2) other dimensionality reduction techniques.
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