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As a fundamental and effective method, sparse representation based classification (SRC) has been applied to computer vision field for many years. However, SRC assumes that the training samples in each class contribute equally to the dictionary which will cause high residual errors and instability. In order to solve the problem and improve classification performance further, class specific centralized...
At present, collaborative representation based classification (CRC) is widely used in many pattern classification and recognition tasks. Meanwhile, spatial pyramid matching (SPM) method, which considers the spatial information in representing the image, is efficient for image classification. However, for SPM, the weights to evaluate the representation of different subregions are fixed. In this paper,...
Classification algorithms based sparse coding have formed a mature system for visual recognition. Recent studies suggest collaborative representation is a much more effective method for classification, compared with sparse representation, the objective function of collaborative representation is constrained by ℓ2-norm. Traditional collaborative representation based classification always uses a set...
Non-negative Matrix Factorization (NMF) has been widely studied and applied to variant computer vision tasks, such as image clustering and pattern classification. Meanwhile, real world stimuli for human neural system (e.g., face images) are usually represented as high-dimensional data vectors rely on graph embedding in original Euclidean space. Thus, the traditional NMF and its variants exhibit weakness...
Recently, dictionary learning based sparse representation algorithm has been widely adopted and achieved satisfying performance in image classification. However, sparse representation based classification (SRC) as well as collaborative representation based classification (CRC) always result in high residual error due to their basic assumption that considers training samples as dictionary directly...
Since Mean Shift algorithm can not track multiple objects, a full automatic multi-object tracking algorithm based on improved Mean Shift is proposed. The background subtraction image kernel density estimation algorithm is used to detect the foreground. The extracted moving objects are used as candidate template to eliminate the influence of background. By adopting object matching based on distance...
In information society, data is become one of the most important part to company or individual. At the same time, data protection has become urgent. Traditional file protection system-based on data encryption has the inherent defects. It can't prevent man-made data destruction and falsification of data. In this paper, using user space file system and authentication server combined with encryption...
This paper investigates the possibility that uses Scale-Invariance Feature Transform (SIFT) feature for face identification. However, it is impossible to employ these SIFT keys,i.e. feature vectors, for identification directly, due to the space incompatible of such SIFT keys. To this end, the Bag-of-words (Bow) vector quantization introduced from scene or text classification is conducted for unifying...
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