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In multi-class problems, within- and between-class scatters should be considered in classification criterion. The common vector approach (CVA) uses the discriminative information obtained from within-class scatter of any class. It has been shown that this classical CVA method gives high recognition rates in multi-class problems. In this study, improvements on the CVA method that consider both within-...
A new simple distance measure has been proposed in which each vector element is weighted in the distance calculation according to its importance as determined by taking its statistics into account. In order to reflect the characteristics of the class, the element-significance factors are calculated based on intraclass variances and mean values of vector elements and utilized in the distance measure...
This paper presents an enhanced version of a non-intrusive measure for assessment of speech quality of voice communication systems and evaluates its performance. The new measure, which uses only the output of the system, is based on measuring perception-based objective auditory distances between voiced parts of the output (processed) speech whose quality is to be evaluated to appropriately matching...
The Common Vector (CV) method is a linear method, which allows to discriminate between classes of data sets, such as those arising in image and word recognition. In this paper a variation of this method is introduced for finding the projection vectors of each class as elements of the intersection of the null space of that class' covariance matrix and the range space of the covariance matrix of the...
The classification process of the Counter Propagation neural network (CPN) is investigated. The homogeneity distribution of the codebook vectors is a key element in the accuracy of the classification process. The paper defines an appropriate homogeneity measure that is strongly correlated with the optimal misclassification error. Based on this homogeneity value, the paper proposes three modification...
This paper presents a new context dependent tone recognition method. First we suggest that there be more than five tone modes in Chinese continuous speech. We get all new tone modes by grouping all tone feature vectors to a specific number of categories. Secondly, we recognize a sentence with the new tone modes and get the new tone sequence. Finally, we find out each original tone of the sentence...
The use of feature vectors obtained by concatenation of different features for text independent speaker identification from clean and telephone speech is studied. The composite feature vectors are examined with GMM and VQ models used to classify speakers. Linear discriminant analysis (LDA), a statistical tool designed to select a reduced set of features for best classification, is applied to enhance...
The script ‘Devanagari’ is used in many Indian languages. Hindi language is also under Devanagari script. In this paper recognition of Hindi characters is done by using a three step procedure. First step is preprocessing, in which binarization of the image and separations of characters are performed. Each Hindi word has a horizontal bar on the top of word. That bar is also removed in preprocessing...
We propose a new framework for image recognition by selectively pooling local visual descriptors, and show its superior discriminative power on fine-grained image classification tasks. The representation is based on selecting the most confident local descriptors for nonlinear function learning using a linear approximation in an embedded higher dimensional space. The advantage of our Selective Pooling...
The personalized music recommender supports the user-favorite songs stored in a huge music database. In order to predict only user-favorite songs, managing user preferences information and genre classification are necessary. In our study, a very short feature vector, obtained from low dimensional projection and already developed audio features, is used for music genre classification problem. We applied...
The word-to-vector (W2V) technique represents words as low-dimensional continuous vectors in such a way that semantic related words are close to each other. This produces a semantic space where a word or a word collection (e.g., a document) can be well represented, and thus lends itself to a multitude of applications including document classification. Our previous study demonstrated that representations...
Despite much success has been achieved, object tracking still remains a challenging research field in computer vision, due to many factors and difficulties such as occlusion, illumination, rotation, pose variance, and intensively motion. To handle them, many classical invariant features, object appearance models, and well-designed but complex tracking frameworks have been proposed. However, they seldom...
Text categorization is an important research in nature language process and content analysis. In this paper, we present latent factor SVM (LF-SVM) for text categorization which use latent factor vectors for category representation on text categorization. We prove that latent factors extracted by PLSA (probability latent semantic analysis) can span convex structure to express text category. Based on...
This work addresses the problemof correspondence matching in multiview video sequences when co-acquired depth maps are available, as in the novel Multiview Video plus Depth (MVD) format. For the purpose of activity-based correspondence matching, we exploit the view depth information, allowing a thorough geometrical analysis of the video scene, and the statistical analysis of the inter-frame differences.
In this paper, we study channel-based authentication, where the receiver can identify and authenticate the senders through channel vectors estimated from their frames. The authentication process is formulated as a sequence of hypothesis test problems. In order to improve the detection probability and reduce the false alarm probability, two schemes are proposed based on different classification algorithms...
The realization of robotic systems that understands human intentions and produces accordingly complex behaviors is needed particularly for disabled persons, and would consequently benefit the aged. For this purpose, a control technique that recognizes human intentions from neural responses called brain machine interface (BMI) have been suggested. The unique ability to communicate with machines by...
The cosine similarity measure is widely used in big data analysis to compare vectors. In this article a new set of vector similarity measures are proposed. New vector similarity measures are based on a multiplication-free operator which requires only additions and sign operations. A vector ‘product’ using the multiplication-free operator is also defined. The new vector product induces the ℓ1-norm...
Nowadays, it is difficult to identify the individual radiation source under low SNR environment. To this problem, the paper proposed a new fractal box dimension based algorithm, to calculate the fractal box dimension of different communication individual radio signals as the subtle characteristics. Basing on the traditional fractal box dimension, the proposed algorithm calculated the derivations of...
Face recognition, one of the most explored themes in biometry, is used in a wide range of applications: access control, forensic detection, surveillance and monitoring system, robotic and human machine interaction. In this paper, a new classifier is proposed for face recognition. The performance of this new classifier is compared with the performance of the KNN classifier. The face image database...
Indirect immunofluorescence (IIF) with HEp-2 cells is considered as a powerful, sensitive and comprehensive technique for analyzing antinuclear autoantibodies (ANAs). Fractal dimension can be used on the analysis of image representing and also on the property quantification like texture complexity and spatial occupation. In this study, we apply the fractal theory in the application of HEp-2 cell staining...
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