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Face Recognition (FR) in surveillance scenarios has attracted the attention of researchers over the last few years. The bottleneck as a large gap in both resolution and contrast between training (high-resolution gallery) and testing (degraded, low quality probes) sets, must be overcome using efficient statistical learning methods. In this paper, we propose(More)
Domain adaptation (DA) is the process in which labeled training samples available from one domain is used to improve the performance of statistical tasks performed on test samples drawn from a different domain. The domain from which the training samples are obtained is termed as the source domain, and the counterpart consisting of the test samples is termed(More)
There is an increasing need for automatically segmenting the regions of different landforms from a multispectral satellite image. The problem of Landform classification using data only from a 3-band optical sensor (IRS-series), in the absence of DEM (Digital Elevation Model) data, is complex due to overlapping and confusing spectral reflectance from several(More)
In this paper, we propose a method to improve the results of clustering in a target domain, using significant information from an auxiliary (source) domain dataset. The applicability of this method concerns the field of transfer learning (or domain adaptation), where the performance of a task (say, classification using clustering) in one domain is improved(More)
This paper describes a method of cross-domain object and event categorization, using the concept of domain adaptation. Here, a classifier is trained using samples from the source/ auxiliary domain and performance is observed on a set of test samples taken from a different domain, termed as the target domain. To overcome the difference between the two(More)