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Videos usually consist of activities involving interactions between multiple actors, sometimes referred to as complex activities. Recognition of such activities requires modeling the spatio-temporal relationships between the actors and their individual variabilities. In this paper, we consider the problem of recognition of complex activities in a video given a query example. We propose a new feature...
Person identification using audio or visual biometrics is a well-studied problem in pattern recognition. In this scenario, both training and testing are done on the same modalities. However, there can be situations where this condition is not valid, i.e. training and testing has to be done on different modalities. This could arise, for example, in covert surveillance. Is there any person specific...
The problem of malicious contents in blogs has reached epic proportions and various efforts are underway to fight it. Blog classification using machine learning techniques is a key method towards doing it. We have devised a machine learning algorithm where features are created from individual sentences in the body of a blog by taking one word at a time. Weights are assigned to the features based on...
Current technology allows steganography applications to conceal any digital file inside of another digital file. Due to the large number of steganography tools available over the Internet, a particular threat exists when criminals use steganography to conceal their activities within digital images in cyber space. In this paper, a set of statistical features are generated using linear mixed effects...
This paper proposes a method for the identification of individuals from their gait using fuzzy logic. Gait signature is first extracted in the form of a spatiotemporal representation called Gait Energy Image (GEI). Since the dimension of GEI is very high, we use fuzzy principal component analysis (FPCA) for dimension reduction. Unlike traditional PCA, it helps to get rid of the problems of outliers...
This paper presents a learning model of multitask pattern recognition (MTPR) which is constructed by several neural classifiers, long-term memories, and the detector of task changes. In the MTPR problem, several multi-class classification tasks are sequentially given to the learning model without notifying their task categories. This implies that the learning model is supposed to detect task changes...
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