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Automatic image aesthetics rating has received a growing interest with the recent breakthrough in deep learning. Although many studies exist for learning a generic or universal aesthetics model, investigation of aesthetics models incorporating individual user’s preference is quite limited. We address this personalized aesthetics problem by showing that individual’s aesthetic preferences exhibit strong...
By analyzing the disadvantages of the traditional KNN using lazy learning that directly classify the data based on the K neighboring classes using the majority voting method, a new Sigmoid weighted classification algorithm WKS (Weighted KNN Based On Sigmoid) was proposed. WKS provides a new method for learning and training, since each training data di ∊ D contributes to the correct classification...
Blind image quality assessment (BIQA) methods aim to estimate the quality of a given test image without referring to the corresponding reference (original) image. Most BIQA methods use visual sensitivity models, which take into consideration intrinsic image characteristics (e.g. contrast, luminance, and texture) to identify degradations and estimate quality. For example, texture-based BIQA methods...
The amount of data produced every day on the internet increases every day and with the increasing popularity of the social networks the number of published photos are huge, and those pictures contain several implicit or explicit brand logos. Detecting this logos in natural images can provide information about how widespread is a brand, discover unwanted copyright distribution, analyze marketing campaigns,...
This paper proposes the design of an adaptive e- learning system with gamification elements. In the context of the increasing need to keep learners motivated among so many distractions, our project aims to help a user acquire knowledge at his own pace, in a captivating environment and as flexible as possible. To achieve that the solution focuses on the course model, adaptive questions and a reward...
Facial expression recognition is a very important research field to understand human emotions. Many facial expression recognition systems have been proposed in the literature over the years. Some of these methods use neural network approaches with deep architectures to address the problem. Although it seems that the facial expression recognition problem has been solved, there is a large difference...
To robustly estimate the pose, classical methods assume some geometrical and temporal assumptions (SfM: Structure from Motion, SLAM: Simultaneous Localization and mapping). These approaches take a pair of images as input and establish correspondences based on global strategy (using the whole image information) or sparse strategy (using key-points features). These correspondences allow solving a set...
Multi-objective optimization plays an important role when one has fitness functions that are somehow conflicting with each other. Also, parameter-dependent machine learning techniques can benefit from such optimization tools. In this paper, we propose a multi-objective-based strategy approach to build compact though representative training sets for Optimum-Path Forest (OPF) learning purposes. Although...
In this paper, we try to recognize negative emotions (sadness and disgust) of human affecting driving by using physiological signals that are commonly used to deal with human emotions. To do this, emotional stimuli are used to induce sadness and disgust, and emotion recognition is performed based on the feature vector extracted from the physiological signals collected on the induced emotion by a stimulus...
In this paper, a text independent speaker recognition system based on Gaussian mixture models (GMM) was developed with a specific focus on the use of a voice activated detector (VAD) algorithm in the training and testing. At the training level, a modified estimation/maximization (EM) algorithm is used. It is less prone to get trapped around a local maximum and so, it will have more chance to converge...
The threat of resource starvation attacks is one of the major problems for the e-Business. More recently these attacks became threats for Cloud environments and Denial of Service is a sub-category of these kinds of attack. The network management is process of taking proactive actions before the attack has taken effect which is responsibility of skilled employees — network managers. In recent time...
The detection of phishing websites using traditional machine learning methods has been demonstrated in previous studies. Traditional machine learning methods assume that the input feature space is the same between the training and testing data. There are scenarios in machine learning, where the available labeled training data has a different input feature space than the testing data. In cases where...
Software fault prediction is one of the significant stages in the software testing process. At this stage, the probability of fault occurrence is predicted based on the documented information of the software systems that are already tested. Using this prior knowledge, developers and testing teams can better manage the testing process. There are many efforts in the field of machine learning to solve...
This paper proposes the SVDNet for retrieval problems, with focus on the application of person re-identification (reID). We view each weight vector within a fully connected (FC) layer in a convolutional neuron network (CNN) as a projection basis. It is observed that the weight vectors are usually highly correlated. This problem leads to correlations among entries of the FC descriptor, and compromises...
We propose ‘Hide-and-Seek’, a weakly-supervised framework that aims to improve object localization in images and action localization in videos. Most existing weakly-supervised methods localize only the most discriminative parts of an object rather than all relevant parts, which leads to suboptimal performance. Our key idea is to hide patches in a training image randomly, forcing the network to seek...
This paper investigates how far a very deep neural network is from attaining close to saturating performance on existing 2D and 3D face alignment datasets. To this end, we make the following 5 contributions: (a) we construct, for the first time, a very strong baseline by combining a state-of-the-art architecture for landmark localization with a state-of-the-art residual block, train it on a very large...
Voice applications often require the ability to make user-friendly responses by judging the user or user-type from an extremely short utterance, such as a single word. However, it is assumed that performance becomes degraded as the utterance length decreases. In this paper, we examine the performance of speaker identification for extremely short utterances of less than two seconds and then study the...
Nowadays, deep learning is very popular in a variety of research field due to its outperformance over the existing machine learning methods and its high generality over raw inputs. According to recent surveys, deep learning can give high performance in visual object recognition system. Human Action Recognition (HAR) is a promising research area over the computer vision research field due to its enormous...
In this paper, we propose a scale-invariant framework based on Convolutional Neural Networks (CNNs). The network exhibits robustness to scale and resolution variations in data. Previous efforts in achieving scale invariance were made on either integrating several variant-specific CNNs or data augmentation. However, these methods did not solve the fundamental problem that CNNs develop different feature...
Contrast of image plays an important role in image perception quality and is also susceptive to various factors during image acquisition process. However, only a few image quality evaluation algorithms have been focused on the contrast-changed image quality assessment (IQA), and none of these methods belongs to blind IQA algorithms. Therefore, they cannot be applied to the case when the reference...
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