International Journal of Innovative Research and Development

T. Abimannan , T. Jayachandran , C. N. Marimuthu

Abstract

Adaptive wavelet-based image characterizations have been proposed in Existing work for content-based image retrieval (CBIR) applications. In this application, the same wavelet basis was used to characterize each query image. This wavelet basis was tuned to maximize the retrieval performance in a training data set. But here a different wavelet basis is used to characterize each query image. A regression function, which is tuned to maximize the retrieval performance in the training data set, is used to estimate the best wavelet filter, i.e., in terms of expected retrieval performance, for each query image. A simple image characterization, which is based on the standardized moments of the wavelet coefficient distributions, is presented. An algorithm is proposed to compute this image characterization almost instantly for every possible separable or no separable wavelet filter. Therefore, using a different wavelet basis for each query image does not considerably increase computation times. On the other hand, significant retrieval performance increases were obtained in a medical image data set, a texture data set, a face recognition data set, and an object picture data set. This additional flexibility in wavelet adaptation paves the way to relevance feedback on image characterization itself and not simply on the way image characterizations are combined.

Keywords

Content-based Image Retrieval (CBIR), Relevance Feedback, Wavelet Adaptation, Wavelet Transform

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L. Prabhu , K. Santhakumar , C. N. Marimuthu

Abstract

The goal of matching unknown faces against a gallery of known people, the face identification is necessary. There are very accurate techniques to perform face identification in controlled environments. but face identification under uncontrolled environments is still an problem. face recognition which considers both shape and texture information to represent face images.The face area is first divided into small regions from which Local Binary Pattern (LBP) histograms are extracted and concatenated into a single, spatially enhanced feature histogram efficiently representing the face image. A large and rich set of feature descriptor for face identification using partial least squares to perform uncontrolled conditions .The method is evaluated on Facial Recognition Technology (FERET) and Face Recognition Grand Challenge (FRGC) data sets .This Algorithms is performed under uncontrolled conditions such as uncontrolled lighting and changes in facial expressions, recognition rates increases Partial Least Squares (PLS) analysis, an efficient dimensionality reduction technique, one which preserves significant discriminative information, to project the data onto a much lower dimensional subspace (20 dimensions reduced from the original 170,000).

Keywords

Face Identification, Feature Combination, Feature Selection, Lbp, Partial Least Squares (PLS)

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V. Logeswari , T. Manickam , C. N. Marimuthu

Abstract

In recent days filters with large lengths are started to use. So parallel processing is essential at any cost. In this paper proposes new parallel FIR filter structures, which are beneficial to symmetric coefficients in terms of the hardware cost, under the condition that the number of taps is a multiple of 2 or 3. The proposed parallel FIR structures use symmetric property to reducing half the number of multipliers in sub filter section at the expense of additional adders in preprocessing and post processing blocks. Exchanging multipliers with adders is advantageous because adders weigh less than multipliers in terms of silicon area; in addition, the overhead from the additional adders in preprocessing and post processing blocks stay fixed and do not increase along with the length of the FIR filter, whereas the number of reduced multipliers increases along with the length of the FIR filter. Parallel FIR filter is essential, especially when the length of the filter is large. Overall, the proposed parallel FIR structures can lead to significant hardware savings for symmetric convolution in odd length from the existing FFA parallel FIR filter, particularly when the length of the filter is large.

Keywords

Digital Signal Processing (DSP), Fast Fir Algorithm (FFAs), Parallel FIR, VLSI

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