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In recent year, healthy is a topic that people concern. It is obviously that eating the food with high amount of calories cause several problems to our health. Recording the amount of calories of the food intake in each meal is one of the stretchy to solve such problem. Although the people can record their meal and discuss with doctors or experts, it is not so convenient and they cannot know the amount...
Traffic signs automatic recognition was researched in this paper. Traffic signs image preprocessing methods was introduced firstly. Secondly, feature extraction algorithm of traffic signs based on SIFT was elaborated, then a fast SIFT algorithm based on PCA dimensionality reduction was presented to extract the characteristics of traffic signs. Finally, the SVM classifier was studied. A large number...
In this paper, we propose the effective similarity feature-based selection and classification algorithm to select similarity features on the training images and to classify face images in face recognition system. The experiments are conducted on The ORL Database of Faces, which consists of 400 images of 40 individuals. Two face recognition systems, one based on the histogram-based feature, and the...
In this paper we present a method of indexing of images from ancient documents based on Bayesian density estimation by the EM algorithm and tangent distance. Initially we present the procedure in case of known density of the mixture to discuss how to spend the density classification and therefore indexing. Once we cleared the problem justifies the choice of the density approximation by Gaussian mixture...
In this study, a new feature-based steganalytic method is presented and four classification methods: Fisher linear discriminant, Gaussian naive Bayes, multilayer perceptron, and k nearest neighbor, are compared for steganalysis of suspicious images. The method exploits statistics of the histogram, wavelet statistics, amplitudes of local extrema from the ID and 2D adjacency histograms, center of mass...
This paper presents a new bag-of-words based algorithm for object recognition. Our algorithm also includes five steps: feature detection and representation, codebook generation, learning and recognition. All features are extracted as dense grids of images instead of interest point for computationally efficiency and effectiveness. While features are described by histograms of oriented gradients (HOG)...
Document clustering is the process of partitioning a set of unlabeled n documents into clusters such that documents in each cluster share some common concepts. Each concept is conveniently represented by some key terms. Using words as features, text data are represented as a vector in a very high dimensional vector space. However, most documents are sparse vectors, for example, more than ten thousand...
A new method for texture classification is presented. The proposed method uses only 3 circular filters. Images are first filtered using these filters, then thresholded and averaged over two small neighborhoods. Universal textons are generated without learning from the training sets. 80 universal textons are used for each neighborhood. The feature space is reduced in one neighborhood by grouping into...
Straightforward classification using kernelized SVMs requires evaluating the kernel for a test vector and each of the support vectors. For a class of kernels we show that one can do this much more efficiently. In particular we show that one can build histogram intersection kernel SVMs (IKSVMs) with runtime complexity of the classifier logarithmic in the number of support vectors as opposed to linear...
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