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In this paper, we propose an object recognition technique using higher order statistics without the combinatorial explosion of time and memory complexity. The proposed technique is a fusion of two popular algorithms in the literature, Independent Component Analysis (ICA) and Support Vector Machines (SVM). We propose to use ICA to reduce the redundancy in the images and obtain some feature vectors...
Local image features have been proven to be a powerful way to describe pattern of interest, both from single objects and complex scenes. While learning from images represented by local features is challenging, recent publications and developments in object recognition has shown that significant performance achievements can be achieved by carefully combining multi-level, coarse-to-fine, sparsely distributed...
Obtaining invariant representation of time varying signals is one of the major problems in object recognition. Recently, a new method that slowly feature analysis (SFA) which can extract invariant features of temporally varying signals is being explored, which is an extension of independent component analysis (ICA) which has been used for extracting facial feature. The technique of SFA can be extended...
Each moving object contains particular unique signatures that can be used for pattern classification via object recognition and identification. Information extracted from the spatial object feature recognition can be provided by independent basis functions to represent actual physical attributes of the moving objects. Compared with principal component analysis, independent component analysis is a...
Recently, a number of empirical studies have compared the performance of PCA and ICA as feature extraction methods in appearance-based object recognition systems, with mixed and seemingly contradictory results. In this paper, we briefly describe the connection between the two methods and argue that whitened PCA may yield identical results to ICA in some cases. Furthermore, we describe the specific...
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