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This paper presents a method based on S-transform and artificial neural network for detection and classification of power quality disturbances. The input features of the neural network are extracted using S-transform. The features obtained from the S-transform are distinct, understandable and immune to noise. These features after normalization are given to a feed forward neural network trained by...
This paper develops a supervised discriminant technique, called margin maximum embedding discriminant (MMED), for dimensionality reduction of high-dimensional data. In graph embedding, our objective is to find a linear transform matrix to make the samples in the same class as compact as possible and the samples belong to the different classes as dispersed as possible. The proposed method effectively...
The paper proposes a method for the classification of EEG signal based on machine learning methods. We analyzed the data from an EEG experiment consisting of affective picture stimuli presentation, and tested automatic recognition of the individual emotional states from the EEG signal using Bayes classifier. The mean accuracy was about 75 percent, but we were not able to select universal features...
A combined texture- and color-based skin detection is proposed in this paper. Nonsubsampled contourlet transform is used to represent texture of the whole image. Local neighbor contourlet coefficients of a pixel are used as feature vectors to classify each pixel. Dimensionality reduction is addressed through principal component analysis (PCA) to remedy the curse of dimensionality in the training phase...
Cell enumeration in peripheral blood smears and cell are widely applied in biological and pathological practice. Not every area in the smear is appropriate for enumeration due to severe cell clumping or sparseness arising from smear preparation. The automatic selection of good areas for cell enumeration can reduce manual labor and provide objective and consistent results. However, this has been infrequently...
This paper presents an experimental comparison of different approaches to learning from multi-labeled video data. We compare state-of-the-art multi-label learning methods on the Media mill Challenge dataset. We employ MPEG-7 and SIFT-based global image descriptors independently and in conjunction using variations of the stacking approach for their fusion. We evaluate the results comparing the different...
In this paper, we propose a method for steganalysis of grayscale images using both spatial and Gabor features. The basis of our work is to use Gabor filter coefficients and statistics of the graylevel co-occurrence matrix of images to train a support vector machine. We show that this feature set works well in steganalysis of grayscale images steganographied by LSB matching and S-tools.
We present a novel classification scheme which uses partial object information that is selected adaptively using modified distance transform and represented as moment invariants (Hu moments) to compensate for scale, translation and rotational transformation(s). The moment invariants of different parts of an object are learned using AdaBoost algorithm [1]. The classifier obtained using the proposed...
In this paper, we propose a novel feature extraction scheme based on the multi-resolution curvelet transform for face recognition. The obtained curvelet coefficients act as the feature set for classification, and are used to train the ensemble-based discriminant learning approach, capable of taking advantage of both the boosting and LDA (BLDA) techniques. The proposed method CV-BLDA has been extensively...
A fast subspace analysis and feature extraction algorithm is proposed which is based on fast Haar transform and integral vector. In rapid object detection and conventional binary subspace learning, Haar-like functions have been frequently used but true Haar functions are seldom employed. In this paper we have shown that true Haar functions can be successfully used to accelerate subspace analysis and...
In this paper we present a novel appearance based approach to the problem of face pose classification. This method suggests the subject-independent pose classification of face images using bilateral filtering and wavelet transform as preprocessing and isometric projection based subspace learning for the extracting of discriminant feature vectors. Our proposed method is evaluated on a large image set...
In recent years, studies of similar music retrieval have been conducted actively. However, because the similarity of music is based on subjective measures, the systems need to be adaptive to user preference. In this paper, we propose an effective method for adaptive similar music retrieval reflecting the user preference by nonlinear feature space transformation based on relevance feedback. The user's...
In this paper, we propose a new information hiding technique without embedding any information into the target content by using neural network trained on frequency domain. Proposed method can detect a hidden bit codes from the content by processing the selected feature subblocks into the trained neural network. Hidden codes are retrieved from the neural network only with the proper extraction key...
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