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Identity safekeeping on chats has recently become an important problem on social networks. One of the most important issues is identity theft, where impostors steal the identity of a person, substituting her in the chats, in order to have access to private information. In the literature, the problem has been addressed by designing sets of features which capture the way a person interacts through the...
Image and video classification is a challenging task, particularly for complex real-world data. Recent work indicates that using multiple features can improve classification significantly, and that score fusion is effective. In this work, we propose a robust score fusion approach which learns non-linear score calibrations for multiple base classifier scores. Through calibration, original base classifiers...
Until recently, inference on fully connected graphs of pixel labels for scene understanding has been computationally expensive, so fast methods have focussed on neighbour connections and unary computation. However, with efficient CRF methods for inference on fully connected graphs, the opportunity exists for exploring other approaches. In this paper, we present a fast approach that calculates unary...
Information describing the materials that make up scene constituents provides invaluable context that can lead to a better understanding of images. We would like to obtain such material information at every pixel, in arbitrary images, regardless of the objects involved. In this paper, we introduce visual material traits to achieve this. Material traits, such as "shiny," or "woven,"...
In this paper, we introduce the concept of proximity priors into semantic segmentation in order to discourage the presence of certain object classes (such as 'sheep' and 'wolf') 'in the vicinity' of each other. 'Vicinity' encompasses spatial distance as well as specific spatial directions simultaneously, e.g. 'plates' are found directly above 'tables', but do not fly over them. In this sense, our...
Tracking by detection techniques have recently been gaining popularity and showing promising results. They use samples classified in previous frames to detect an object in a new frame. However, because they rely on self updating, such techniques are prone to object drift. Multiple classifier systems can be used to improve the detection over that of a single classifier. However, such techniques can...
We introduce a new method for representing the dynamics of human-object interactions in videos. Previous algorithms tend to focus on modeling the spatial relationships between objects and actors, but ignore the evolving nature of this relationship through time. Our algorithm captures the dynamic nature of human-object interactions by modeling how these patterns evolve with respect to time. Our experiments...
Automatic facial point detection plays arguably the most important role in face analysis. Several methods have been proposed which reported their results on databases of both constrained and unconstrained conditions. Most of these databases provide annotations with different mark-ups and in some cases the are problems related to the accuracy of the fiducial points. The aforementioned issues as well...
In this paper we propose a novel street scene semantic parsing framework, which takes advantage of 3D point clouds captured by a high-definition LiDAR laser scanner. Local 3D geometrical features extracted from subsets of point clouds are classified by trained boosted decision trees and then corresponding image segments are labeled with semantic classes e.g. buildings, road, sky etc. In contrast to...
Local surface description is a critical stage for feature matching and recognition tasks. This paper presents a rotation invariant local surface descriptor, called 3D-Div. The proposed descriptor is based on the concept of 3D vector field's divergence, extensively used in electromagnetic theory. To generate a 3D-Div descriptor of a 3D surface, a local surface patch is parameterized around a randomly...
Visual tracking has attracted a significant attention in the last few decades. The recent surge in the number of publications on tracking-related problems have made it almost impossible to follow the developments in the field. One of the reasons is that there is a lack of commonly accepted annotated data-sets and standardized evaluation protocols that would allow objective comparison of different...
Real scene video surveillance always involves low resolutions, lack of illumination or cluttered environments, which leads to insufficiency of discriminative details for the target. In this situation, discrimination based tracking methods could fail. To address this problem, this paper presents an adaptive multi-feature integration method in terms of feature invariance, which can evaluate the stability...
Visual tracking of objects under varying lighting conditions and changes of the object appearance, such as articulation and change of aspect, is a challenging problem. Due to its robustness and speed, distribution field tracking is among the state-of-the-art approaches for tracking objects with constant size in grayscale sequences. According to the theory of averaged shifted histograms, distribution...
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