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We present a method to improve the performance of content-based image retrieval (CBIR) systems. The idea is based on the concept of query models [1], which generalizes the notion of similarity in multi-feature queries. In a query model features are organized in layers. Each succeeding layer has to investigate only a subset of the image set the preceding layer had to examine. For the purpose of performance...
In this paper, we describe a method for video summarization that operates on a video segment level. We formulate this problem as the one of automatic video segment selection based on a learning process that employs salient video segment paradigms. We design an hierarchical learning scheme that consists of two steps. At the first step, an unsupervised process is performed in order to determine salient...
Spatial filtering is useful for extracting features from multichannel EEG signals. In order to enhance robustness of the spatial filter against low SNR and small samples, we propose a smoothing method for the spatial filter using spectral graph theory. This method is based on an assumption that the electrodes installed in nearby locations observe the electrical activities of the same source. Therefore...
The handwritten character recognition (HCR) is the major problem in the character recognition domain. There are a lot of methods applied to the handwritten character recognition problems. The "Extreme Learning Machine" (ELM) is the one among them. ELM is the single hidden layer neural networks widely applied in many applications and classification problems. The features of ELM are faster...
Feature extraction is an essential step in pattern classification, which is normally divided into two tasks: transforming the input vector into a feature vector and/or reducing its dimensionality. A well-defined feature extraction algorithm makes the subsequent classification process more effective and efficient. One of the most important feature extraction algorithms is linear discriminant analysis...
Benefiting from its openness, collaboration and real-time features, Micro blog has become one of the most important news communication media in modern society. However, it is also filled with fake news. Without verification, such information could spread promptly through social network and result in serious consequences. To evaluate news credibility on Micro blog, we propose a hierarchical propagation...
This paper presents a novel pricing method for maximizing the profit of a cloud provider. Mostly, there are three different instances (on-demand, reserved, and spot instances) in big cloud providers. Each instance has its own characteristics. A user may choose one of the instances regarding his requirements and instance types. In this paper, different characteristics of instance types have been extracted...
Multiple features have been employed for content-based medical image retrieval. To reduce curse of dimensionality, subspace learning techniques have been applied to learn a low-dimensional subspace from multiple features. Most of the existing methods have two drawbacks: first, they ignore the fact that multiple features have complementary properties, and thus have different contributions to construct...
Traditional text classification technology based on machine learning and data mining techniques has made a big progress. However, it is still a big problem on how to draw an exact decision boundary between relevant and irrelevant objects in binary classification due to much uncertainty produced in the process of the traditional algorithms. The proposed model CTTC (Centroid Training for Text Classification)...
A scheme for the feature level fusion of two behavioral biometrics speech and signature using fusion method weighted sum is proposed. Feature reduction is performed using modified feature selection algorithm based on Pollination based optimization which has never been applied to the problem earlier. The modified algorithm is applied to the fusion method to search the feature space for optimal and...
In this paper are presented simple and practical solutions to extrinsic calibration between a camera and a 2D laser sensor, without overlap. Previous methods utilized a plane or an intersecting line of two planes as a geometric constraint with enough common field-of-view. These required additional sensors to calibrate non-overlapping systems. In this paper, we present two methods for solving the problem...
In this paper, we propose multi-objective differential evolution (DE) based feature selection and ensemble learning techniques for biomedical entity extraction. The algorithm operates in two layers, first step of which concerns with the problem of automatic feature selection for a machine learning algorithm, namely Conditional Random Field (CRF). The solutions of the final best population provides...
This paper proposes to use Genetic algorithm for optimizing the best Eigen vectors to improve the recognition accuracy of Modular image Principal Component Analysis (MIPCA) for face recognition. Modular Image PCA has been proved to be efficient in extracting features for recognizing face invariant to large expression. It is important to note that all the extracted features are not efficient and required...
Metric learning to learn a good distance metric for distinguishing different people while being insensitive to intra-person variations is widely applied to person re-identification. In previous works, local histograms are densely sampled to extract spatially localized information of each person image. The extracted local histograms are then concatenated into one vector that is used as an input of...
Although multi-view datasets have become more accessible in the real-world applications, most state-of-the-art action recognition methods applied to those datasets rely on simple view agreement when combining local information from various views together. This leads to deteriorated performance in situations with view insufficiency and view disagreements. In this paper, we propose a novel framework...
Due to the ambiguity in describing and discriminating between clothing images of different styles, it has been a challenging task to solve clothing image characterization problems. Based on the use of multiple types of visual features, we propose a novel multi-view nonnegative matrix factorization (NMF) algorithm for solving the above task. Our multi-view NMF not only observes image representations...
The partial pattern matching is fundamental for pattern recognition to compare the pair of input patterns by exploiting the common features shared by those patterns while excluding the irrelevant ones. In this paper, for the pattern matching, we propose a novel method of smoothly structured sparse canonical correlation analysis, called S3CCA. The proposed method works on the feature matrix composed...
Meta-heuristic-based feature selection has been paramount in the last years, mainly because of its simplicity, effectiveness and also efficiency in some cases. Such approaches are based on the social dynamics of living organisms, and can vary from birds, bees, bats and ants. Very recently, an optimization algorithm based on krill herd (KH) was proposed for continuous-valued applications, and it has...
Feature extraction plays an important role in analyzing data with multivariate features. Linear discriminant analysis based on L1-norm (LDA-L1) is a recently developed technique for enhancing the robustness of the classic LDA against outliers. However, LDA-L1 employs a greedy strategy to find all the discriminant vectors, which may lead to suboptimal solution. To address this issue, we develop a novel...
Compilation optimization is critical for software performance. Before a product releases, the most effective algorithm combination should be chosen to minimize the object file size or to maximize the running speed. Compilers like GCC usually have hundreds of optimization algorithms, in which they have complex relationships. Different combinations of algorithms will lead to object files with different...
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