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In the recent years, the evolution in multimedia technology has accelerated the growth of multimedia data. Even though the multimedia data are heterogeneous, the rich information they carry has made a high demand for sophisticated multimedia knowledge discovery systems. To mine the knowledge from multimedia document, each type of multimedia data has to undergo unique processing and knowledge discovery...
In this paper, a stereoscopic video description method is proposed that indirectly incorporates scene geometry information derived from stereo disparity, through the manipulation of video interest points. This approach is flexible and able to cooperate with any monocular low-level feature descriptor. The method is evaluated on the problem of recognizing complex human actions in natural settings, using...
By concerning with the health of the patients, analysis of blood cell particularly morphological structure of leukocyte in microscopic blood smear can effectively detect the important blood disorder such as the Acute Lymphoblastic Leukemia. Unfortunately, the analysis made by hematology expert is not always accurate and rapid due to the error prone modality and operator's incapability's. The presented...
The Mojette transform is a discrete, exact and redundant Radon transform. The application of the Mojette transform for lossless image compression is based on image projection similarity using different directions, with intra-projection coding, inter-projection coding and differential coding schemes being applied. For the latter case, we propose mean coding of projections to improve the Mojette transform...
Object tracking is an important task within the field of computer vision. Tracking accuracy depends mainly on finding good discriminative features to estimate the target location. In this paper, we introduce online feature learning in tracking and propose to learn good features to track generic objects using online convolutional neural networks (OCNN). OCNN has two feature mapping layers that are...
This paper deals with the recovery of corrupted depth maps in loss-prone networks. Different from color maps, depth maps are not directly used for display but served for view synthesis process. Therefore, the conventional concealment methods which focus on reducing the distortion of reconstructed color maps, are not suitable for the corrupted depth maps. In this paper, a novel mode selection method...
Moving object detection in the presence of changing illumination and non-stationary background such as swaying of trees, fountains, ripples in water, flag fluttering in the wind, camera jitters, noise, etc., is known to be very difficult and challenging task. Background subtraction (BS) is the most sought after technique for moving object detection. Still, most of the BS techniques do not take into...
This paper shows a work done under Affective Computing umbrella and in the field of emotion recognition. The paper explores the anatomy of a human face and builds the classification model based on it. The anatomical information of face is used to locate several points on the face and to extract the features. The features are in form of distance vectors which can be of specific person or group of persons...
This paper addresses the problem of diagnosis of diseases on cotton leaf using Principle Component Analysis (PCA), Nearest Neighbourhood Classifier (KNN). Cotton leaf data analysis aims to study the diseases pattern which are defined as any deterioration of normal physiological functions of plants, producing characteristic symptoms in terms of undesirable color changes mainly occurs upon leaves; caused...
In this paper, we propose a new approach to data density estimation based on the total sum of distances from a data point, and the recently introduced Recursive Density Estimation technique. It is suitable for autonomous real-time video analytics problems, and has been specifically designed to be executed very fast; it uses integer-only arithmetic with no divisions and no floating point numbers (no...
In this work we propose a new key frame extraction method based on SIFT local features. We extracted feature vectors from a carefully selected group of frames from a video shot, analyzing those vectors to eliminate near duplicate key frames, helping to keep a compact set. Moreover, as the key frame extraction is based on local features, it keeps frames latent semantics and, therefore, helps to keep...
Graph classification has traditionally focused on graphs generated from a single feature view. In many applications, it is common to have useful information from different channels/views to describe objects, which naturally results in a new representation with multiple graphs generated from different feature views being used to describe one object. In this paper, we formulate a new Multi-Graph-View...
This paper introduces a novel approach for image edit. Instead of solving poisson equation, image edit task can be treated as in painting the region of interest by propagating the boundary differences between target and source image. Based on the Fast Marching Method, differences along the boundary can be progressively eliminated to interior layer by layer, and we can get a seamless edit result. It...
Querying of nearest neighbour (NN) elements on large data collections is an important task for several information or content retrieval tasks. In the paper Local Hash-indexing tree (LHI-tree) is introduced, which is a disk-based index scheme that uses RAM for quick space partition localization and hard disks for the hash indexing. When large collections are considered, such hybrid data structure should...
Cricket broadcast video analysis has had difficulty identifying aspects of the content such as the type of batting stroke or the direction of the field played towards. Here we construct a composite feature combining Optical flow analysis along with camera view analysis to model the type of shots played. The work first presents an improved camera shot analysis based on learning parameters from a small...
Limited size of object images and lack amount of training data would degrade the performance seriously in modern-day recognition applications. Therefore, how to effectively utilize available information from images becomes more and more important. In this paper, we propose to extend the linear regression classification (GLRC), which can effectively use all the information in cases of multiple inputs,...
In this work we present an extension of the SIFT algorithm to color images. In the extrema detection stage, an energy level descriptor based on the color tensor of the image is computed and used to locate keypoints candidates. Then, in the description stage, the color gradient magnitude and orientation of the samples around the keypoint are used to compute an orientation histogram to create the keypoint...
Many attempts have been made to identify the region of interest in an image. In this paper, we have provided a new approach for ROI detection using the output of image annotation. Our claim is that because ROI is a subjective concept, a method should be used to diagnosis human mental models and for this purpose, we have used KNN base annotation in our method. Because many people in pictures that are...
In different color spaces, the three color channels might have different relationship, but most of color face recognition methods exploit the color information in a simple way. In this paper, we propose a novel hybrid fusion scheme for color face recognition, which first uses two-phase test sample representation (TPTSR) to obtain matching scores of each color channel of the test sample and then uses...
Visual information retrieval has become a major research area due to increasing rate at which images are generated in many application. This paper addresses an important problems related to the content-based images retrieval. It concerns the vector representation of images and its proper use in image retrieval. Indeed, we propose a new model of content-based image retrieval allowing to integrate theories...
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