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The clustering algorithm by fast search and find of density peaks is shown to be a promising clustering approach. However, this algorithm involves manual selection of cluster centers, which is not convenient in practical applications. In this paper we discuss the correlation between density peaks and cluster centers. As a result, we present a new local density estimation method to highlight the uniqueness...
We introduce a new algorithm that maps multiple instance data using both positive and negative target concepts into a data representation suitable for standard classification. Multiple instance data are characterized by bags which are in turn characterized by a variable number of feature vectors or instances. Each bag has a known positive or negative label, but the labels of any given instances within...
While most existing video summarization approaches aim to extract an informative summary of a single video, we propose a novel framework for summarizing multi-view videos by exploiting both intra- and inter-view content correlations in a joint embedding space. We learn the embedding by minimizing an objective function that has two terms: one due to intra-view correlations and another due to inter-view...
Video summarization is useful to find a concise representation of the original video, nevertheless its evaluation is somewhat challenging. This paper proposes a simple and efficient method for precisely evaluating the video summaries produced by the existing techniques. This method includes two steps. The first step is to establish a set of matched frames between automatic summary (AT) and the ground...
This paper proposes an object verification method by using sparse representation (SR) which has been applied for object representation and recognition. However, SR dictionary does not show sufficient compactness. Our method comprises three major modules. First, we train the sparse matrix by using boost K-Singular Value Decomposition (boost K-SVD) to obtain a sparse vector set. Second, we combine two...
The relationship between human behavior and SNS (social networking service) activity is growing stronger every year as SNS websites such as Twitter and Facebook develop and expand. Accordingly, a significant number of studies related to SNS have also been reported. However, the majority of this research, such as that which examines the transmission of information via SNS websites, focuses entirely...
Cocaine dependence devastates millions of human lives. Despite of a variety of treatments, there is a very high rate of individual relapse to drug use. In the last decade, functional magnetic resonance imaging (fMRI) proved to be a powerful tool to diagnose and understand different pathologies. This work provides advances in the identification of cocaine dependence and in the relapse prediction based...
Multi-label classification has attracted many attentions in various fields, such as text categorization and semantic image annotation. Aiming to classify an instance into multiple labels, various multi-label classification methods have been proposed. However, the existing methods typically build models in the identical feature (sub)space for all labels, possibly inconsistent with real-world problems...
In this paper, we focus on describing the method we designed for automatic perceived personality prediction. We present a simple model that uses three different sets of features: nonverbal audio cues, visual cues from video, and facial landmark points. The model uses a random decision forest to do regression from the extracted features. As we discuss in Section 4, this multimodal model performs relatively...
Inferring scene depth from a single monocular image is an essential component in several computer vision applications such as 3D modeling and robotics. This process is an ill-posed problem. To tackle this challenging problem, previous efforts have been focusing on exploiting only global or local depth aware properties. We propose a model that incorporates both of them to obtain significantly more...
In this paper, we focus on studying the effects of various image operations on sensor fingerprint camera identification. It is known that artifacts in the image processing pipeline, such as pixel defects or unevenness of the responses in the CCD array as well black current noise leave telltale footprints. Nowadays, camera identification based on the analysis of these artifacts is a well established...
This study focuses on using ultrasound (US) biomarkers for characterizing myopathies and in particular myositis. US offers an opportunity to deliver diagnostics in clinical settings at a fraction of the cost and discomfort entailed in current workflows. US is also better suited for usage in under-resourced environments. This paper is focused on studying the link between biomarkers related to absolute...
In this paper, we present a novel thermodynamic framework for graphs that can be used to analyze time evolving networks, relating the thermodynamics variables to macroscopic changes in network topology, and linking major structural transition to phase changes in the thermodynamic picture. We start from a recent quantum-mechanical characterization of the structure of a network relating the graph Laplacian...
Document layout segmentation and recognition is an important task in the creation of digitized documents collections, especially when dealing with historical documents. This paper presents an hybrid approach to layout segmentation as well as a strategy to classify document regions, which is applied to the process of digitization of an historical encyclopedia. Our layout analysis method merges a classic...
This paper presents a significant improvement for the synthesis of texture images using convolutional neural networks (CNNs), making use of constraints on the Fourier spectrum of the results. More precisely, the texture synthesis is regarded as a constrained optimization problem, with constraints conditioning both the Fourier spectrum and statistical features learned by CNNs. In contrast with existing...
Judgments about personality based on facial appearance are strong effectors in social decision making, and are known to have impact on areas from presidential elections to jury decisions. Recent work has shown that it is possible to predict perception of memorability, trustworthiness, intelligence and other attributes in human face images. The most successful of these approaches require face images...
The color constancy problem is addressed by structured-output regression on the values of the fully-connected layers of a convolutional neural network. The AlexNet and the VGG are considered and VGG slightly outperformed AlexNet. Best results were obtained with the first fully-connected “fc6” layer and with multi-output support vector regression. Experiments on the SFU Color Checker and Indoor Dataset...
This paper introduces a new light scattering model for surfaces with rough boundaries and absorption. This is an extension to Ragheb-Hancock model. The new model adds an absorption term proportional of the squared cosine of the light incidence angle, and satisfies conservation of energy. To test the accuracy of the model, we have used the CUReT database. The model was compared with alternatives such...
The stability matters in clinical prediction models because it makes the model to be interpretable and generalizable. It is paramount for high dimensional data, which employ sparse models with feature selection ability. We propose a new method to stabilize sparse support vector machines using intrinsic graph structure of the electronic medical records. The graph structure is exploited using the Jaccard...
The problem of re-identify persons across single disjoint camera-pairs has received great attention from the community. Despite this, when the re-identification process has to be carried out on a large camera network a different approach has to be considered. In particular, existing approaches have neglected the importance of the network topology (i.e., the structure of the monitored environment)...
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