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The quality of the training data used in a supervised image classification can impact on the accuracy of the resulting thematic map obtained. Here the effects of mis-labeled training cases on the accuracy of classifications by discriminant analysis and a support vector machine were explored. The accuracy of both classifiers varied with the amount and nature of mis-labeled training cases. In particular,...
This paper studies a new method for identifying the new words, Objective to identify new words better. Method is first to extract the positive and negative samples from training corpus which was handled by segmentation and POS Tagging according to the dictionary, then combining with all kinds of words classification which was gotten from training corpus, and gaining the new word support vector through...
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...
With the widespread use of Internet, the possibilities of exposing confidential data to invaders or attackers increases. Intrusion Detection System (IDS) is used for detecting various intrusions in network environment and to prevent data from malicious attackers. In this paper, a combined algorithm based on Principal Component Analysis (PCA) and Core Vector Machine (CVM), which is an extremely fast...
Important task in image database is to organize images into appropriate category using different features of images. Image classification is studied for many years. There are various techniques proposed to increase the accuracy of classification. In this paper a novel data mining based approach is proposed for content based image classification. Feature extraction and classification algorithms are...
Supervised leaning classifier is usually constructing based on models through learning to achieve high accuracy. Support Vector Machine (SVM) is more useful classification technique in supervised learning model. In this paper, we examined SVM with linear kernel function and pre-computed kernel function using micro array data sets. In this observation is focused major three aspects such as accuracy,...
We propose a novel rule-based model to incorporate contextual information and effect of negation that enhances the performance of sentiment classification performed using bag-of-words models. We employed morphological analysis in feature extraction to ensure feature vector contains only opinionated words in a textual review. Also it reduces the dimensionality of feature vector and, eventually improves...
Locally normalized Histogram of Oriented Gradient (HOG) algorithm originally proposed by Dalal & Triggs presents excellent results for pedestrian detection. However, as the demand of accuracy and speed in real-time application increase, the detection speed and robustness of this method is becoming insufficient. Over the years, improvements have been proposed by different researchers in order to...
User experience of smart mobile devices can be improved in numerous scenarios with the assist of in-air gesture recognition. Most existing methods proposed by industry and academia are based on special sensors. On the contrary, a special sensor-independent in-air gesture recognition method named Dolphin is proposed in this paper which can be applied to off-the-shelf smart devices directly. The only...
This paper proposes a novel method of detecting packed executable files using steganalysis, primarily targeting the detection of obfuscated malware through packing. Considering that over 80% of malware in the wild is packed, detection accuracy and low false negative rates are important properties of malware detection methods. Experimental results outlined in this paper reveal that the proposed approach...
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...
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...
The high spectral and spatial resolution of hyperspectral images increases the capability to distinguish physical materials and objects, presenting new challenges to image analysis and classification. In fact, many studies have been conducted to extract and integrate spectral and contextual information in the classification process. However, the availability of various spatial features (e.g. morphological...
In this paper, we propose a new image annotation method by combining content-based image annotation and tag-based image annotation techniques. Content-based image annotation technique is adopted to extract "loosely defined concepts" by analyzing pre-given images' features such as color moment (CM), edge orientation histogram (EOH), and local binary pattern (LBP), followed by constructing...
Auditory perception of surrounding environment is important to machine awareness. To provide artificial awareness ability for machines, a bio-inspired salient environmental sound detection and recognition method is proposed. The salient sounds are detected by using the auditory saliency map which based on heterogeneous saliency features from visual and acoustic domain. Spectral and temporal saliency...
Scene classification is a hot problem in the computer vision. In this paper, a novel rapid scene classification method is proposed based on the discrete cosine transform (DCT) domain. Firstly, we divided the whole image into several areas of the same size without repetition, do DCT transform with the size of the В ∗ В in each separate sub image area divided above. Secondly, scanning AC coefficients...
In this work, we discuss the benefits of image compression on FITS image files to perform image retrieval tasks on the enormous NASA Solar Dynamics Observatory (SDO) image repository. With the objective of making solar image files more portable and easy to distribute and archive, we test several lossless compression algorithms as well as lossy compression algorithms in order to determine the rate...
Visual objects decoding with functional magnetic resonance imaging (fMRI) often merely depends on the brain activity, using pattern classification to decode information about visual stimuli from patterns of activity. However, the spatial resolution of fMRI is still limited to the > mm range. Limited by its spatial resolution, fMRI voxels lack high-spatial-frequency information of visual stimuli...
A texture descriptor based on a set of indices of degrees of local approximating polynomials is proposed in this paper. An image is split into non-overlapping patches, reshaped into one-dimensional source vectors and convolved with the polynomial approximation kernels of various degrees p. As a result, a set of approximations is obtained. For each element of the source vector, these approximations...
Languages are used as a tool for human to communicate their needs to one another. To be able to use any language human beings need time to learn to achieve understanding. The newborn babies use their cries by their instinct to communicate their needs. The difference cries of the infant can indicate different requirements. This work proposes a method to determine the meanings of infant cries according...
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