Markov chain based computational visual attention model that learns from eye tracking data
We use Markov chain to model the visual attention.Our visual attention model is based on low level and high level image features.We use the real eye tracking data to train our visual attention model.We measure performances of attention models by ...
Deterministic discrete tomography reconstruction by energy minimization method on the triangular grid
Binary tomography reconstruction problem for triangular grid is considered and analyzed.A new deterministic reconstruction method (SPG-T) is proposed.Experiments on hexagonal shape test images are presented and analyzed.Performance comparison of SPG-T ...
Session compensation using binary speech representation for speaker recognition
We aim to present the power of a new speech representation, the Speaker Binary Key.New variant of the within-class scatter matrix for session compensation is proposed.Covariance matrix using common attributes contains much more information.i-Vector and ...
A nonparametric approach to region-of-interest detection in wide-angle views
We model motion patterns using distribution of 3D structure tensor-based features.We use a nonparametric approach to learn the distribution of features.The approach detect both spatial and temporal regions-of-interest in videos.Qualitative and ...
Feature selection using Principal Component Analysis for massive retweet detection
Social networks become a major actor in massive information propagation. In the context of the Twitter platform, its popularity is due in part to the capability of relaying messages (i.e. tweets) posted by users. This particular mechanism, called ...
View invariant action recognition using generalized 4D features
We recognize actions independently of viewpoints using generalized 4D features.We developed new 4D-STIPs with 3D space volumes by extending the widely used 3D-STIPs.Arbitrary view point can be generated by projecting 3D space volumes and 4D-STIPs.We ...
Activity-based methods for person recognition in motion capture sequences
We present two algorithms for person recognition from motion capture data.The first algorithm is based on a similarity measure between two sequences.The second algorithm combines dimensionality reduction and a Bag of Words approach.Correct person ...
A novel phase congruency based descriptor for dynamic facial expression analysis
New feature extraction to describe a dynamic event, providing both temporal and spatial information.Capability of the spatio-temporal descriptor to detect the motion patterns in a video.Able to deal with different image resolution and illumination ...
Local value difference metric
Value difference metric (VDM) is one of the widely used distance functions.We propose local value difference metric (LVDM).LVDM uses a modified decision tree algorithm to find the neighborhood of the test instance.The experimental results on 36 UCI ...
Robust human silhouette extraction with Laplacian fitting
Human silhouette extraction has been a primary step to estimate human poses or classify activities from videos. While the accuracy of human silhouettes has great impact on the follow-on human pose/gait estimation, it has been important to guarantee the ...
Entropy-based outlier detection using semi-supervised approach with few positive examples
Outlier detection is an important problem in data mining that aims to discover useful exceptional and unusual patterns hidden in large data sets. Fraud detection, time series monitoring, intrusion detection and medical condition monitoring are some of ...
Kernel Reference Discriminant Analysis
Linear Discriminant Analysis (LDA) and its nonlinear version Kernel Discriminant Analysis (KDA) are well-known and widely used techniques for supervised feature extraction and dimensionality reduction. They determine an optimal discriminant space for (...
Revisiting the Fisher vector for fine-grained classification
Wining method of Fine-grain image classification challenge 2013.Late combination of two indexing and classification strategies.Good practices for fine grain image classification.Key features: descriptors filtering, spatial coordinates coding, active ...
Efficient classification using the Euler characteristic
A topological object descriptor using Euler characteristics of subsets is proposed.The EC-Graph descriptor encodes useful information about the spatial distribution.An efficient method for calculating a series of EC values is proposed.An efficient ...
A kernel support vector machine-based feature selection approach for recognizing Flying Apsaras' streamers in the Dunhuang Grotto Murals, China
Define the shape-based features of Flying Apsaras' streamers.Propose a morphological descriptor of incorporating these features for KSVM.Demonstrate the suitability of the descriptor and KSVM for streamer recognition. Recognizing Flying Apsaras' ...
Denoising by semi-supervised kernel PCA preimaging
We incorporate label information into the kernel PCA denoising procedure through generalization of semi-supervised kernel PCA.We provide an efficient fixed-point iteration for the pre-image problem based on a graph regularized kernel.We demonstrate that ...
Removal of high-intensity impulse noise by Weber's law Noise Identifier
New impulse noise detector named as Weber's law Noise Identifier (WLNI).WLNI method detects the noise for higher noise intensity100% of the times.Modifies the Switching Median Filter by considering the two cases. High-intensity impulse noise removal is ...
An improved set covering problem for Isomap supervised landmark selection
A novel algorithm for Isomap supervised landmark selection is presented.It relies on a weighted set covering problem solved via Lagrangian relaxation with subgradient optimization.The proposed technique empirically dominated prominent competing ...
Adaptive conformal semi-supervised vector quantization for dissimilarity data
Existing semi-supervised learning algorithms focus on vectorial data given in Euclidean space. But many real life data are non-metric, given as (dis-)similarities which are not widely addressed. We propose a conformal prototype-based classifier for ...
Chronological classification of ancient paintings using appearance and shape features
A method for classifying ancient paintings in chronology.A bag-of-visual-words approach that uses appearance features and shape features.A deep-learning network that refines the appearance features.Algorithm evaluation using a collection of 660 ancient ...
Cell morphology based classification for red cells in blood smear images
The proposed a hybrid neural network architecture for red cell classification.The separation of overlapping cells have been attempted.Utilized all visual features extracted from red cell images with new classifier.Improvement in red cell normality ...
A new extracting algorithm of k nearest neighbors searching for point clouds
We propose an extracting algorithm for k nearest neighbors searching.Vector inner product instead of distance calculation for distance comparison.Extracting algorithm can integrate with any other algorithm as plug-in.Two prominent algorithms and seven ...
Interval prediction for graded multi-label classification
We deal with graded multi-label classification tasks.Our approach is an improvement over other techniques applied to this kind of problems.We propose the use of nondeterministic classifiers to improve the reliability of classifications.This approach has ...
Leaf margins as sequences
We propose a high-level interpretation of leaf contours through CSS analysis.Leaf margins are described by an original string representation of vectorial symbols.Methods inspired from string processing are adapted to this objects.We use this spatially-...
Discriminant Bag of Words based representation for human action recognition
Human action recognition based on Bag of Words representation.Discriminant codebook learning for better action class discrimination.Unified framework for the determination of both the optimized codebook and linear data projections. In this paper we ...
Classification of clouds in satellite imagery using over-complete dictionary via sparse representation
Satellite imagery provides distribution information of cloud in a wide range of spatial and temporal scales. To improve the accuracy of weather forecasting and enhancing the effectiveness of climate monitoring, it is important to study the ...
Real-time local stereo via edge-aware disparity propagation
This letter presents a novel method for real-time local stereo matching. Different from previous methods which have spent many efforts on cost aggregation, the proposed method re-solves the stereo problem by propagating disparities in the cost domain. ...
Learning high-dimensional networks with nonlinear interactions by a novel tree-embedded graphical model
We propose a novel network learning method that can detect network structure with both linear and nonlinear interactions.Most existing network learning methods focus on linear interactions.Integration of generalized linear model, sparse learning, and ...