The Experts below are selected from a list of 192 Experts worldwide ranked by ideXlab platform

Guiguang Ding - One of the best experts on this subject based on the ideXlab platform.

  • Discrete Probability Distribution prediction of image emotions with shared sparse learning
    IEEE Transactions on Affective Computing, 2018
    Co-Authors: Sicheng Zhao, Guiguang Ding, Xin Zhao, Youbao Tang, Qingming Huang
    Abstract:

    Computationally modelling the affective content of images has been extensively studied recently because of its wide applications in entertainment, advertisement, and education. Significant progress has been made on designing discriminative features to bridge the affective gap. Assuming that viewers can reach a consensus on the emotion of images, most existing works focused on assigning the dominant emotion category or the average dimension values to an image. However, the image emotions perceived by viewers are subjective by nature with the influence of personal and situational factors. In this paper, we propose a novel machine learning approach that characterizes the categorical image emotions as a Discrete Probability Distribution (DPD). To associate emotion with the visual features extracted from images, we present shared sparse learning to learn the combination coefficients, with which the DPD of an unseen image is predicted by linearly combining the DPDs of the training images. Furthermore, we extend our method to the setup where multi-features are available and learn the optimal weights for each feature to reflect the importance of different features. Extensive experiments are carried out on Abstract, Emotion6 and IESN datasets and the results demonstrate the superiority of the proposed method, as compared to the state-of-the-art approaches.

  • approximating Discrete Probability Distribution of image emotions by multi modal features fusion
    International Joint Conference on Artificial Intelligence, 2017
    Co-Authors: Sicheng Zhao, Guiguang Ding
    Abstract:

    Existing works on image emotion recognition mainly assigned the dominant emotion category or average dimension values to an image based on the assumption that viewers can reach a consensus on the emotion of images. However, the image emotions perceived by viewers are subjective by nature and highly related to the personal and situational factors. On the other hand, image emotions can be conveyed by different features, such as semantics and aesthetics. In this paper, we propose a novel machine learning approach that formulates the categorical image emotions as a Discrete Probability Distribution (DPD). To associate emotions with the extracted visual features, we present a weighted multi-modal shared sparse leaning to learn the combination coefficients, with which the DPD of an unseen image can be predicted by linearly integrating the DPDs of the training images. The representation abilities of different modalities are jointly explored and the optimal weight of each modality is automatically learned. Extensive experiments on three datasets verify the superiority of the proposed method, as compared to the state-of-the-art.

  • IJCAI - Approximating Discrete Probability Distribution of image emotions by multi-modal features fusion
    Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017
    Co-Authors: Sicheng Zhao, Guiguang Ding
    Abstract:

    Existing works on image emotion recognition mainly assigned the dominant emotion category or average dimension values to an image based on the assumption that viewers can reach a consensus on the emotion of images. However, the image emotions perceived by viewers are subjective by nature and highly related to the personal and situational factors. On the other hand, image emotions can be conveyed by different features, such as semantics and aesthetics. In this paper, we propose a novel machine learning approach that formulates the categorical image emotions as a Discrete Probability Distribution (DPD). To associate emotions with the extracted visual features, we present a weighted multi-modal shared sparse leaning to learn the combination coefficients, with which the DPD of an unseen image can be predicted by linearly integrating the DPDs of the training images. The representation abilities of different modalities are jointly explored and the optimal weight of each modality is automatically learned. Extensive experiments on three datasets verify the superiority of the proposed method, as compared to the state-of-the-art.

Sicheng Zhao - One of the best experts on this subject based on the ideXlab platform.

  • Discrete Probability Distribution prediction of image emotions with shared sparse learning
    IEEE Transactions on Affective Computing, 2018
    Co-Authors: Sicheng Zhao, Guiguang Ding, Xin Zhao, Youbao Tang, Qingming Huang
    Abstract:

    Computationally modelling the affective content of images has been extensively studied recently because of its wide applications in entertainment, advertisement, and education. Significant progress has been made on designing discriminative features to bridge the affective gap. Assuming that viewers can reach a consensus on the emotion of images, most existing works focused on assigning the dominant emotion category or the average dimension values to an image. However, the image emotions perceived by viewers are subjective by nature with the influence of personal and situational factors. In this paper, we propose a novel machine learning approach that characterizes the categorical image emotions as a Discrete Probability Distribution (DPD). To associate emotion with the visual features extracted from images, we present shared sparse learning to learn the combination coefficients, with which the DPD of an unseen image is predicted by linearly combining the DPDs of the training images. Furthermore, we extend our method to the setup where multi-features are available and learn the optimal weights for each feature to reflect the importance of different features. Extensive experiments are carried out on Abstract, Emotion6 and IESN datasets and the results demonstrate the superiority of the proposed method, as compared to the state-of-the-art approaches.

  • approximating Discrete Probability Distribution of image emotions by multi modal features fusion
    International Joint Conference on Artificial Intelligence, 2017
    Co-Authors: Sicheng Zhao, Guiguang Ding
    Abstract:

    Existing works on image emotion recognition mainly assigned the dominant emotion category or average dimension values to an image based on the assumption that viewers can reach a consensus on the emotion of images. However, the image emotions perceived by viewers are subjective by nature and highly related to the personal and situational factors. On the other hand, image emotions can be conveyed by different features, such as semantics and aesthetics. In this paper, we propose a novel machine learning approach that formulates the categorical image emotions as a Discrete Probability Distribution (DPD). To associate emotions with the extracted visual features, we present a weighted multi-modal shared sparse leaning to learn the combination coefficients, with which the DPD of an unseen image can be predicted by linearly integrating the DPDs of the training images. The representation abilities of different modalities are jointly explored and the optimal weight of each modality is automatically learned. Extensive experiments on three datasets verify the superiority of the proposed method, as compared to the state-of-the-art.

  • IJCAI - Approximating Discrete Probability Distribution of image emotions by multi-modal features fusion
    Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017
    Co-Authors: Sicheng Zhao, Guiguang Ding
    Abstract:

    Existing works on image emotion recognition mainly assigned the dominant emotion category or average dimension values to an image based on the assumption that viewers can reach a consensus on the emotion of images. However, the image emotions perceived by viewers are subjective by nature and highly related to the personal and situational factors. On the other hand, image emotions can be conveyed by different features, such as semantics and aesthetics. In this paper, we propose a novel machine learning approach that formulates the categorical image emotions as a Discrete Probability Distribution (DPD). To associate emotions with the extracted visual features, we present a weighted multi-modal shared sparse leaning to learn the combination coefficients, with which the DPD of an unseen image can be predicted by linearly integrating the DPDs of the training images. The representation abilities of different modalities are jointly explored and the optimal weight of each modality is automatically learned. Extensive experiments on three datasets verify the superiority of the proposed method, as compared to the state-of-the-art.

  • predicting Discrete Probability Distribution of image emotions
    International Conference on Image Processing, 2015
    Co-Authors: Sicheng Zhao, Xiaolei Jiang
    Abstract:

    Most existing works on affective image classification tried to assign a dominant emotion category to an image. However, this is often insufficient, as the emotions that are evoked in viewers by an image are highly subjective and different. In this paper, we propose to predict the Probability Distribution of categorical image emotions. Firstly we extract commonly used features of different levels for each image. Then we formulize the emotion Distribution prediction as a shared sparse leaning problem, which is optimized by iteratively reweighted least squares. Besides, we introduce three baseline algorithms. Experiments are carried out on a dataset of peer rated abstract paintings and the results demonstrate the superiority of our proposed method, as compared to some state-of-the-art approaches.

Michael C Grierson - One of the best experts on this subject based on the ideXlab platform.

  • Estimating the Discrete Probability Distribution of the age characteristic of Veteran populations using SAS ® , SAS/OR ® and SAS Simulation Studio ® for use in population projection models
    2020
    Co-Authors: Michael C Grierson
    Abstract:

    This paper examines the Discrete Probability Distribution of the age characteristic of the Veteran population in the US. The basis of the examination is the American Community Survey 1 (ACS) and the Social Security Administrations (SSA) period life table for males in 2007. 2 The ACS is stratified to efficiently represent population elements including the population of Veterans. The current Veteran population Discrete Probability Distribution for age is examined as a starting point. The survey also supports examining the expected future age of Veteran Discrete Probability Distributions. The paper will show how to use an efficiently designed stratified survey (the 1 year ACS) to support a simulation that moves that population into the future and provides a basis for estimating future population age Distributions and their consequent impact on organization cost and budget planning. Combining the processing of stratified survey data with Operations Research tools (like the Discrete event simulator) and supporting subsequent analysis of results is a combined capability that is not matched elsewhere.

  • estimating the Discrete Probability Distribution of the age characteristic of veteran populations using sas sas or and sas simulation studio for use in population projection models
    2012
    Co-Authors: Michael C Grierson
    Abstract:

    This paper examines the Discrete Probability Distribution of the age characteristic of the Veteran population in the US. The basis of the examination is the American Community Survey 1 (ACS) and the Social Security Administrations (SSA) period life table for males in 2007. 2 The ACS is stratified to efficiently represent population elements including the population of Veterans. The current Veteran population Discrete Probability Distribution for age is examined as a starting point. The survey also supports examining the expected future age of Veteran Discrete Probability Distributions. The paper will show how to use an efficiently designed stratified survey (the 1 year ACS) to support a simulation that moves that population into the future and provides a basis for estimating future population age Distributions and their consequent impact on organization cost and budget planning. Combining the processing of stratified survey data with Operations Research tools (like the Discrete event simulator) and supporting subsequent analysis of results is a combined capability that is not matched elsewhere.

Konstantinos Priftis - One of the best experts on this subject based on the ideXlab platform.

  • Computing and maximizing the exact reliability of wireless backhaul networks
    2017
    Co-Authors: David Coudert, James Luedtke, Eduardo Moreno, Konstantinos Priftis
    Abstract:

    The reliability of a fixed wireless backhaul network is the Probability that the network can meet all the communication requirements considering the uncertainty (e.g., due to weather) in the maximum capacity of each link. We provide an algorithm to compute the exact reliability of a backhaul network, given a Discrete Probability Distribution on the possible capacities available at each link. The algorithm computes a conditional Probability tree, where at each leaf in the tree a valid routing for the network is evaluated. Any such tree provides bounds on the reliability, and the algorithm improves these bounds by branching in the tree. We also consider the problem of determining the topology and configuration of a backhaul network that maximizes reliability subject to a limited budget. We provide an algorithm that exploits properties of the conditional Probability tree used to calculate reliability of a given network design, and we evaluate its computational efficiency.

  • Computing and maximizing the exact reliability of wireless backhaul networks
    2016
    Co-Authors: David Coudert, James Luedtke, Eduardo Moreno, Konstantinos Priftis
    Abstract:

    The reliability of a fixed wireless backhaul network is the Probability that the network can meet all the communication requirements considering the uncertainty (e.g., due to weather) in the maximum capacity of each link. We provide an algorithm to compute the exact reliability of a backhaul network, given a Discrete Probability Distribution on the possible capacities available at each link. The algorithm computes a conditional Probability tree, where each leaf in the tree requires a valid routing for the network. Any such tree provides an upper and lower bound on the reliability, and the algorithm improves these bounds by branching in the tree. We also consider the problem of determining the topology and configuration of a backhaul network that maximizes reliability subject to a limited budget. We provide an algorithm that exploits properties of the conditional Probability tree used to calculate reliability of a given network design. We perform a computational study demonstrating that the proposed methods can calculate reliability of large backhaul networks, and can optimize topology for modest size networks.

D Bellot - One of the best experts on this subject based on the ideXlab platform.

  • approximate Discrete Probability Distribution representation using a multi resolution binary tree
    International Conference on Tools with Artificial Intelligence, 2003
    Co-Authors: D Bellot
    Abstract:

    Computing and storing probabilities is a hard problem as soon as one has to deal with complex Distributions over multiples random variables. The problem of efficient representation of Probability Distributions is central in term of computational efficiency in the field of probabilistic reasoning. The main problem arises when dealing with joint Probability Distributions over a set of random variables: they are always represented using huge Probability arrays. In this paper, a new method based on a binary-tree representation is introduced in order to store efficiently very large joint Distributions. Our approach approximates any multidimensional joint Distributions using an adaptive discretization of the space. We make the assumption that the lower is the Probability mass of a particular region of feature space, the larger is the discretization step. This assumption leads to a very optimized representation in term of time and memory. The other advantages of our approach are the ability to refine dynamically the Distribution every time it is needed leading to a more accurate representation of the Probability Distribution and to an anytime representation of the Distribution.