A new method has been presented to compare the performance of textural features for characterization and classi cation of SAR (Synthetic Aperture Radar) images. In contrast to the conventional comparative studies based on classi cation accuracy, this method emphasizes the sensitivity of texture measures for grey level transformation and multiplicative noise of di V erent speckle levels. Texture features based on grey level run length, texture spectrum, power spectrum, fractal dimension and co-occurrence have been considered. A number of image samples of built-up, barren land, orchard and sand regions were considered for the study. The interpretation of the results is expected to provide useful information for the remote sensing community, which employs textural features for segmentation and classi cation of satellite images.

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The quality assessment of images is meaningful for most video and images applications. Recently a new metric called Region of Interest structure similarity index (ROI-SSIM) is proposed for assessing image quality with better reflection to human visual characteristics than traditional image quality assessment methods. In this paper, five different filtering algorithms are compared based on the ability to reconstruct noise affected images using ROI-SSIM. Experimental results give us the quality assessment of filtering algorithms based on region of interest.

Transductive learning is a special case of semi-supervised learning, where class labels to the test patterns alone are found. That is, the domain of the learner is the test set alone. Often, transductive learners achieve a better classification accuracy, since additional information in the form of test patterns location in the feature-space is used. For several inductive learners, there exists corresponding transductive learners; like for SVMs there exists transductive SVMs (TSVMs). For nearest neighbor based classifiers, their corresponding transductive methods are achieved through graph mincuts or spectral graph mincuts. It is shown that these solutions achieve low leave-one-out cross-validation (LOOCV) error with respect to nearest neighbor based classifiers. It is formally shown in the paper that, through a clustering method, it is possible to get various solutions having zero LOOCV error with respect to nearest neighbor based classifiers. Some solutions can have low classification accuracy. The paper proposes, instead of optimizing LOOCV error, to optimize a margin like criterion. This criterion is based on the observation that similar labeled patterns should be nearer to each other, while dissimilar labeled patterns should be far away. An approximate method to solve the proposed optimization problem is given in the paper which is called selective incremental transductive nearest neighbor classifier (SI-TNNC). SI-TNNC finds the test pattern from the test set which is very close to one class of training patterns and at the same time very much away from the other class of training examples. The selected test pattern is given its nearest neighbor's label and is added to the training set. This pattern is removed from the test set. The process is repeated with the next best test pattern, and is stopped only when the test set becomes empty. An algorithm to implement SI-TNNC method is given in the paper which has a quadratic time complexity. Other related solutions have either cubic time complexity or are NP-hard. Experimentally, using several standard data-sets, it is shown that the proposed transductive learner achieves on-par or better classification accuracy than its related competitors.

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India has grown into a fairly large-sized system, offering opportunities for education and training in a wide variety of trades and disciplines at certificate, diploma, degree, postgraduate degree and doctoral levels in institutions located throughout the country. In the year 1947-48, the country had 38 degree level institutions with intake capacity of 2,500 and 53 diploma level institutions with intake capacity of 3,670. The intake for postgraduates was 70. There was rapid expansion of the system in the next 20 years. In 1967-68, the number of degree level institutions had increased to 137 with intake capacity of 25,000. In the next 10 years, the system capacity increased only marginally to admit 30,000 students for degree courses (Anandakrishnan, M. (2006). The system capacities have increased very rapidly in the 20 years, with the major role being played by the private sector. By 1997, the system had 547 degree level institutions with admission capacity of about 131,000.