LBP maps for improving fractal based texture classification
This paper presents an innovative manner of obtaining discriminative texture signatures by using the LBP approach to extract additional sources of information from an input image and by using fractal dimension to calculate features from these sources. ...
Medical image retrieval using deep convolutional neural network
Selection of publicly available medical images having 24 classes and 5 modalities.Classification of multimodal medical images by deep convolutional neural network.Content based medical image retrieval using with and without class predictions.Improved ...
Upper bound of Bayesian generalization error in non-negative matrix factorization
Non-negative matrix factorization (NMF) is a new knowledge discovery method that is used for text mining, signal processing, bioinformatics, and consumer analysis. However, its basic property as a learning machine is not yet clarified, as it is not a ...
Desynchronization-based congestion suppression for a star-type Internet system with arbitrary dimension
Suppression of synchronization is a central issue of the Internet congestion control. Current desynchronization scheme suffers from the breakdown of fairness. We propose a desynchronization scheme by introducing state feedback controllers in such a way ...
Scene text segmentation using low variation extremal regions and sorting based character grouping
Extraction of textual information from natural scene images is a challenging task due to imaging conditions and diversity of text properties. Segmentation of scene text is important step in the pipeline that significantly affects the final recognition ...
Detecting shot boundary with sparse coding for video summarization
Keyframe selection is a common way to summarize video contents. However, delimiting shot boundaries to extract a representative keyframe from each shot is not trivial as most shot boundary techniques are heuristic and sensitive to the types of video ...
Graph-based discriminative nonnegative matrix factorization with label information
Nonnegative matrix factorization (NMF) is a very effective technique for image representation, which has been widely applied in computer vision and pattern recognition. This is because it can capture the underlying intrinsic structure of data by using ...
Adaptive community detection in complex networks using genetic algorithms
Community detection is a challenging optimisation problem that consists in searching for communities that belong to a network or graph under the assumption that the nodes of the same community share properties that enable the detection of new ...
Stochastic stabilization of genetic regulatory networks
This paper is concerned with the stochastic stabilization for genetic regulatory networks. Based on the Lyapunov stability theory in combination with certain convex algorithm, we obtain the sufficient condition under which the unstable genetic ...
A new history experience replay design for model-free adaptive dynamic programming
An adaptive dynamic programming (ADP) controller is a powerful control technique that has been investigated, designed and tested in a wide range of applications for solving optimal control problems in complex systems. The performance of the ADP ...
An efficient level set model with self-similarity for texture segmentation
A texture energy term based on the local self-similarity texture descriptor is introduced to the LGDF model, which could effectively snap to the textures boundary.A lattice Boltzmann method (LBM) is deployed as a new numerical scheme to solve the level ...
Spatiotemporal salient object detection based on distance transform and energy optimization
In this paper, we present a novel spatiotemporal salient object detection method to produce high-quality saliency maps. The gradient of optical flow is adopted to coarsely locate the boundaries of salient object and the gray-weighted distance transform ...
Bag-of-words feature representation for blind image quality assessment with local quantized pattern
No-reference/blind image quality assessment (BIQA) is designed to measure the image quality without any knowledge of the reference image. Most existing BIQA metrics employ natural scene statistics or learning based models, which have achieved great ...
Adaptive finite-time control for overlapping cluster synchronization in coupled complex networks
An overlapping cluster mathematical model is proposed.Several extended conditions are proposed to be appropriate not only for this model, but also for traditional cluster model.A novel control is designed for overlapping community networks to refine ...
Partially hidden Markov models for privacy-preserving modeling of indoor trajectories
Markov models are natural tools for modeling trajectories, following the principle that recent location history is predictive of near-future directions. In this work, we study Markov models for describing and predicting human movement in indoor spaces, ...
Multi-level structured hybrid forest for joint head detection and pose estimation
In real-world applications, factors such as illumination variation, occlusion, and poor image quality, etc. make head detection and pose estimation much more challenging. In this paper, we propose a multi-level structured hybrid forest (MSHF) for joint ...
Trace ratio 2DLDA with L1-norm optimization
We develop a non-greedy algorithm to solve the trace ratio 2DLDA with L1-norm optimization.Our algorithm solves trace ratio L1-2DLDA, while most existing L1-2dLDA methods do not.Our algorithm can maximize the trace ratio objective function, which is ...
A multi-kernel framework with nonparallel support vector machine
Multiple kernel learning (MKL) serves as an attractive research direction in current kernel machine learning field. It can flexibly process diverse characteristics of patterns such as heterogeneous information or irregular data, non-flat distribution of ...
Learning multiple local binary descriptors for image matching
Binary descriptors have received extensive research interests due to their low memory storage and computational efficiency. However, the discriminative ability of the binary descriptors is often limited in comparison with general floating point ones. In ...
Merging Students-t and Rayleigh distributions regression mixture model for clustering time-series
This paper addresses an efficient scheme for clustering time-series through a novel regression mixture strategy (RMM) that simultaneously utilizes the benefits of the Markov random field (MRF) and mean template. Each component of the proposed RMM is a ...
Pinning synchronization of complex dynamical networks with and without time-varying delay
The pinning synchronization in two types of complex dynamical networks are studied in this paper. In the first one, the nodes are coupled by their states; In the second one, the nodes are coupled by their past states or delayed states. By designing ...
Learning visual saliency from human fixations for stereoscopic images
In the previous years, a lot of saliency detection algorithms have been designed for saliency computation of visual content. Recently, stereoscopic display techniques have developed rapidly, which results in much requirement of stereoscopic saliency ...
Kernel quaternion principal component analysis and its application in RGB-D object recognition
While the existing quaternion principal component analysis (QPCA) is a linear tool developed mainly for processing linear quaternion signals, the quaternion representation (QR) used in QPCA creates redundancy when representing a color image signal of ...
Neural network-based adaptive fault tolerant consensus control for a class of high order multiagent systems with input quantization and time-varying parameters
This paper studies the adaptive leader-following consensus control for a class of strict-feedback multi-agent systems. All the agents possess the quantized inputs, the time-varying unknown parameters and the actuator failures. By estimating the upper ...
Deep learning algorithms for discriminant autoencoding
In this paper, a new family of Autoencoders (AE) for dimensionality reduction as well as class discrimination is proposed, using various class separating methods which cause a translation of the reconstructed data in a way such that the classes are ...
Bag-of-concepts
Document vectors are represented as the bag of concepts.Concepts are created by clustering term vectors generated from word2vec.The proposed method shows superior representational performance.Document vectors generated from the proposed method have ...
Addressing class-imbalance in multi-label learning via two-stage multi-label hypernetwork
Multi-label learning is concerned with learning from data examples that are represented by a single feature vector while associated with multiple labels simultaneously. Existing multi-label learning approaches mainly focus on exploiting label ...
Skew t mixture of experts
Mixture of experts (MoE) is a popular framework in the fields of statistics and machine learning for modeling heterogeneity in data for regression, classification and clustering. MoE for continuous data are usually based on the normal distribution. ...
A spatially cohesive superpixel model for image noise level estimation
Estimating image noise levels is a critical task for many image processing applications, where the detection of homogeneous regions always plays a key role. Most conventional methods empirically divide the images into rectangular blocks and then select ...