Feasibility of conventional neural networks for content-based image retrieval in big data Online publication date: Wed, 03-Nov-2021
by M.A. Muthiah; E. Logashanmugam
International Journal of Public Sector Performance Management (IJPSPM), Vol. 8, No. 3, 2021
Abstract: In real-time applications, content-based image retrieval (CBIR) has to be done with high level of accuracy and less latency. In this paper, feasibility of conventional neural networks for image retrieval is studied. A research database consisting of 8,000 images in 102 different categories is considered. Four sets of image features are obtained, i.e., moments (up to order 6) from the intensity of the images, moments from the discrete cosine transform (DCT) coefficients of the images, moments from each of the four sub-bands of the wavelet decomposed image (reverse biorthogonal wavelets) and moments from the discrete Walsh Hadamard transform (DWHT) coefficients of the images. Different sets of the dataset are used for training, testing and validation of the networks. The performance is measured in terms of accuracy. From the analysis, it is found that PNN provides the highest accuracy for DWT features.
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