GLOBAL AND LOCAL DESCRIPTOR FOR CBIR AND IMAGE ENHANCEMENT USING MULTI-FEATURE FUSION METHOD

Devrat Arya; Jaimala Jha

doi:10.29121/granthaalayah.v4.i6.2016.2651

Authors

Devrat Arya Department of CSE/IT, MITS, Gwalior, INDIA
JaimalaJha Professor, Department of CSE/IT, MITS, Gwalior, INDIA

DOI:

https://doi.org/10.29121/granthaalayah.v4.i6.2016.2651

Keywords:

Image Retrieval, HSV Color Space, Global Correlation Vector, DWT, DGVC, SVM

Abstract [English]

The research is ongoing in CBIR it is getting much popular. In this retrieval of image is done using a technique that searches the necessary features of image. The main work of CBIR is to get retrieve efficient, perfect and fast results.In this algorithm, fused multi-feature for color, texture and figure features. A global and local descriptor (GLD) is proposed in this paper, called Global Correlation Descriptor (GCD) and Discrete Wavelet Transform (DWT), to excerpt color and surface feature respectively so that these features have the same effect in CBIR. In addition, Global Correlation Vector (GCV) and Directional Global Correlation Vector (DGCV) is proposed in this paper which can integrate the advantages of histogram statistics and Color Structure Descriptor (CSD) to characterize color and consistency features respectively. Also, this paper is implemented by Hu moment (HM) for shape feature, it extract 8 moments for image. For the classification process, apply kernel Support vector machine (SVM). The experimental result has computed precision, recall, f_measure and execution time. Also, worked on two datasets: Corel-1000 and Soccer-280.

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