Arvind Tolambiya

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A modified forward-only counterpropagation neural network (MFO-CPN) for color image compression is proposed. It uses several higher-order distance measures for calculating winning node. It also incorporates nonlinear adjustment of learning rates in both the layers. Results with these distance functions are compared. Proposed modifications leads to(More)
Two dimensional principal component analyses (2DPCA) is recently proposed technique for face representation and recognition. The standard PCA works on 1-dimensional vectors which has inherent problem of dealing with high dimensional vector space data such as images, whereas 2DPCA directly works on matrices i.e. in 2DPCA, PCA technique is applied directly on(More)
This paper introduces the use of Relevance Vector Machines (RVMs) for content based image classification and compares it with the conventional Support Vector Machine (SVM) approach. Different wavelet kernels are included in the formulation of the RVM. We also propose a new wavelet based feature extraction method that extracts lesser number of features as(More)
In this paper, we presented a practical and effective image compression system based on Wavelet Support Vector Machine (WSVM) with Morlet wavelet kernel for compressing still images. The algorithm combines WSVM learning with discrete wavelet decomposition technique. Compression is achieved by approximating wavelet coefficients at each subband separately(More)
Image Compression is solved by using Wavelet-Modified Single Layer Linear Forward Only Counter Propagation Network (MSLLFOCPN) technique. Form the wavelets it inherits the properties of localizing the global spatial and frequency correlation from wavelets. Function approximation and prediction are obtained from neural networks. As a result counter(More)
This paper presents a practical and effective image compression system based on wavelet decomposition and contrast sensitive-SVR (support vector regression) for compressing still images. The kernel function in an SVR plays the central role of implicitly mapping the input vector (through an inner product) into a high-dimensional feature space. We study the(More)
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