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Automatically recognising facial emotions has drawn increasing attention in computer vision. Facial landmark based methods are one of the most widely used approaches to perform this task. However, these approaches do not provide good performance. Thus, researchers usually tend to combine more information such as textural and audio information to increase the recognition rate. In this paper we propose...
This paper presents a sparse representation based image inpainting method using local patch analysis and geometric structure based feature extraction. In local patch analysis, we approximate the target region by weighted average of some local patches which are frequently occurred within a neighborhood. Local patch statistics is applied to find the most relevant neighbors for each target patch. Further...
In this paper, we describe a one-class classification method based on Support Vector Data Description, which exploits multiple graph structures in its optimization process. We derive in a generic solution which can be employed for supervised one-class classification tasks. The devised method can produce linear or non-linear decision functions, depending on the adopted kernel function. In our experiments,...
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...
The Liquid State Machine (LSM) is a biologically plausible model of computation for recurrent spiking neural networks, which offers promising solutions to real-world applications in both software and hardware based systems. At the same time, deep feedforward rate-based neural networks such as convolutional neural networks (CNNs) have achieved great success in many computer vision related applications...
This paper addresses the problem of automatic machine analysis based severity scoring of psoriasis skin disease. Three different disease parameters namely, erythema, scaling and induration are considered for such severity grading. Given an image containing a psoriatic plaque the task is to predict severity scores for all the three parameters. This paper presents a novel deep CNN based architecture...
Although both feature dependencies and label dependencies are crucial for facial action unit (AU) recognition, little work addresses them simultaneously till now. To address this limitation, we propose a 4-layer Restrict Boltzmann Machine (RBM) to simultaneously capture feature level and AU level dependencies to recognize multiple AUs. Specifically, the bottom two layers of the RBM model capture dependencies...
In this paper, we propose a novel entropic signature for graphs, where we probe the graphs by means of continuous-time quantum walks. More precisely, we characterise the structure of a graph through its average mixing matrix. The average mixing matrix is a doubly-stochastic matrix that encapsulates the time-averaged behaviour of a continuous-time quantum walk on the graph, i.e., the ij-th element...
Many of the existing tracking methods do not estimate the object scale (width, height), only the location (x, y). In this paper we present a method which can accurately estimate the object scale given the location. The proposed approach works by cascading two methods together; such that each method refines the estimate by removing the false scale samples. Our method does not depend on the tracking...
In clustering applications, multiple views of the data are often available. Although clustering could be done within each view independently, exploiting information across views is promising to gain clustering accuracy improvement. A common assumption in the field of multi-view learning is that the clustering results from multiple views should be consistent with a latent clustering. However, the potential...
Person re-identification (Re-ID) maintains a global identity for an individual while he moves along a large area covered by multiple cameras. Re-ID enables a multi-camera monitoring of individual activity that is critical for surveillance systems. However, the low-resolution images combined with the different poses, illumination conditions and camera viewpoints make person Re-ID a challenging problem...
In this work, two enhancement methods are proposed to speed up junction detection performed by the JUDOCA detector. The first enhancement method minimizes the number of junction candidates on which the circular kernel is applied. This is achieved by introducing a suppression technique that takes both the thin and thick edge images into consideration. The second method works on relaxing the step of...
Multiple kernel learning methods combine a set of base kernels to produce an optimal one for a certain classification or regression problem. But selecting a set of base kernels from a plethora of kernels is not automated. We provide a criteria to select efficient base kernels. Automating the selection process of efficient base kernel requires less time and effort than manually selecting them. However,...
In this paper, we develop a new transitive aligned Weisfeiler-Lehman subtree kernel. This kernel not only overcomes the shortcoming of ignoring correspondence information between isomorphic substructures that arises in existing R-convolution kernels, but also guarantees the transitivity between the correspondence information that is not available for existing matching kernels. Our kernel outperforms...
This work introduces the one-class slab SVM (OCSSVM), a one-class classifier that aims at improving the performance of the one-class SVM. The proposed strategy reduces the false positive rate and increases the accuracy of detecting instances from novel classes. To this end, it uses two parallel hyperplanes to learn the normal region of the decision scores of the target class. OCSSVM extends one-class...
In this paper, we explore the usage of deep learning based solutions in fine grained activity recognition in the wild. As a powerful tool, deep learning has been widely used in image classification, object detection and activity recognition. We focus on implementing deep learning methods into the more complicated fine grained activity recognition problems. We test our solutions on MPII activity dataset...
In this paper we aim at increasing the descriptive power of the covariance matrix, limited in capturing linear mutual dependencies between variables only. We present a rigorous and principled mathematical pipeline to recover the kernel trick for computing the covariance matrix, enhancing it to model more complex, non-linear relationships conveyed by the raw data. To this end, we propose Kernelized-COV,...
Deep Learning (DL), especially Convolutional Neural Networks (CNN), has become the state-of-the-art for a variety of pattern recognition issues. Technological developments have allowed the use of high-end General Purpose Graphic Processor Units (GPGPU) for accelerating numerical problem solving. They resort no only to lower computational time, but also allow considering much larger networks. Hence,...
Supervised learning of convolutional neural networks (CNNs) can require very large amounts of labeled data. Labeling thousands or millions of training examples can be extremely time consuming and costly. One direction towards addressing this problem is to create features from unlabeled data. In this paper we propose a new method for training a CNN, with no need for labeled instances. This method for...
Feature matching plays an important role in many computer vision applications, such as object recognition, scene reconstruction or image mosaicing. In this paper, we propose an algorithm called Hessian ORB - Overlapped FREAK (HOOFR) which is based on the combination of the ORB detector and the FREAK bio-inspired descriptor. We address some modifications related to the detection and the description...
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