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In this paper, the lip feature that has the highest correlation with audio features is investigated. Audio features are selected as Mel Frequency Cepstral Coefficients (MFCC) of the audio signal. Three different lip features are considered for the visual lip information, where these features are 2D DCT coefficients of the intensity based image and the optical flow vectors within the lip region, and...
Due to the rapid evolution in multimedia technology, the multimedia data have been growing at a phenomenal rate. With the enormous amount of multimedia data, the richness of its information has raised the demand for sophisticated multimedia knowledge discovery systems. Multimedia documents requires distinct type of processing and knowledge discovery methods, due to the distinct characteristics of...
A framework that can be used for assessing the suitability of different feature vectors in the task of determining the age similarity between a pair of faces is introduced. This framework involves the use of a dataset containing images displaying compounded types of variation along with the use of an ideal dataset, containing pairs of age-separated face images captured under identical imaging conditions...
The problem of training a classifier from a handful of positive examples, without having to supply class specific negatives is of great practical importance. The proposed approach to solving this problem builds on the idea of training LDA classifiers using only class specific foreground images and a large collection of unlabelled images, as described in [11]. While we adopt the LDA training methodology...
Key frame based video summarization, which enables an user to access any video in a friendly and meaningful way, has emerged as an important area of research for the multimedia community. Various pattern clustering techniques are applied for the extraction of key frames from a video to form a storyboard. In this work, we improve existing Delaunay graph based video summarization framework with i) semantic...
Detection of multi-manipulated image has always been a more realistic direction for digital image forensic technologies, which extremely attracts interests of researchers. However, mutual affects of manipulations make it difficult to identify the process using existing single-manipulated detection methods. In this paper, a novel algorithm for detecting image manipulation history of blurring and sharpening...
In recent years one of the most important problems in blind image quality assessment is to achieving perceptual model that can predict the quality of distorted images completely blind. It means the model should perform without any learning process and by as little knowledge about their distortion as possible. Most previously methods measure the quality of an image degraded by a single degradation...
With shorter calibration times and higher information transfer rates, steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have been studied most activity in recent years. Target identification is the ongoing core task in BCI researches, and plays a significant role in practical applications. In order to improve the performance of SSVEP-based BCI system, we proposed...
In this paper, a novel method is proposed for the Traffic Sign Recognition (TSR) using the Principle Component Analysis (PCA) and the Multi-Layer Perceptrons (MLPs) network. In particular, the candidate signs are individually detected from two chroma components in the YCbCr space and then classified into three shape classes: circle, square, and triangle based on computing the rotated version correlations...
Ill-posed linear inverse problems (IPLIP), such as restoration and reconstruction, are core topics of transient feature extraction in cyclostationary signal processing. This paper presents a novel method for extracting transients through exploiting both the wavelet basis and the majorization minimization algorithm. To solve the objective function, majorization minimization (MM) algorithm is applied...
In this paper we consider the problem of matching feature points between multi-source images. Straightforwardly comparing visual features such as SIFT may result in desirable local feature correspondences for single-source images. However, when it comes to matching feature between multi-source images, e.g. between an RGB color image and an infrared image, the feature based matching scheme tends to...
In content-based image retrieval, and for this critical issue of image feature fusion, paper proposes a new method to determine the weights for multi-feature fusion. In this paper, color histogram, color correlogram, gray level co-occurrence matrix, Tamura and Hu moments, this five kinds of feature extraction method was adopted. Firstly, use these five features conducted single feature retrieval on...
With the advances in neuroimaging technology, it is now possible to measure human brain activity with increasing temporal and spatial resolution. This vast amount of spatio-temporal data requires the development of computational methods capable of building an integrated picture of the functional networks for a better understanding of the healthy and diseased brain [1]. Although the construction of...
Sepsis is a systemic deleterious host response to infection. It is a major healthcare problem that affects millions of patients every year in the intensive care units (ICUs) worldwide. Despite the fact that ICU patients are heavily instrumented with physiological sensors, early sepsis detection remains challenging, perhaps because clinicians identify sepsis by using static scores derived from bed-side...
In this paper, we propose a novel dynamic ensemble selection framework using meta-learning. The framework is divided into three steps. In the first step, the pool of classifiers is generated from the training data. The second phase is responsible to extract the meta-features and train the meta-classifier. Five distinct sets of meta-features are proposed, each one corresponding to a different criterion...
Discriminative feature extraction (DFE) is an effective linear dimensionality reduction method for pattern recognition. It improves the recognition performance via optimizing subspace projection axes and classifier parameters simultaneously. In this paper, we propose a nonlinear extension of DFE, called discriminative quadratic feature extraction (DQFE), for which feature vectors are firstly mapped...
Research in automated human gait recognition has largely focused on developing robust feature representation and matching algorithms. In this paper, we investigate the possibility of clustering gait patterns based on the features extracted by automated gait matchers. In this regard, a k-means based clustering approach is used to categorize the feature sets extracted by three different gait matchers...
Obsessive Compulsive Disorder (OCD) is a frequent, chronic disorder producing intrusive thoughts which results in repetitive behaviors. It is thought that this psychological disorder occurs due to abnormal functional connectivity in certain regions of the brain called Default Mode Network (DMN) mainly. Recently, functional MRI (FMRI) studies were performed in order to compare the differences in brain...
Processing bug reports plays an important role for software maintenance. Recently, the issue of detecting duplicate bug reports has been noticed due to their considerable appearances. In the past, many NLP-based detection schemes have been proposed. However, the cluster-level correlation relationships are not extensively considered in the past studies. In this paper, we present an improved detection...
In this paper the Conditional Restricted Boltzmann Machine (CRBM) is employed in the context of unsupervised audio segmentation. The CRBM acts as a temporal modeling method and learns, from a maximum likelihood perspective, the temporal relationships of the feature vectors that have been extracted from a large corpus of training data. After the CRBM has been trained, we quantify the correlation of...
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