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公开(公告)号：US20170109603A1
公开(公告)日：2017-04-20
申请号：US15395331
申请日：2016-12-30
发明人： James Z. Wang , Xin Lu , Poonam Suryanarayan , Reginald B. Adams, Jr. , Jia Li , Michelle Newman
CPC分类号： G06K9/4671 , G06F17/30259 , G06F17/30268 , G06K9/00302 , G06K9/00684 , G06K9/00711 , G06K9/46 , G06K9/4604 , G06K9/52 , G06K9/6267 , G06T7/60 , G06T11/206 , G06T2207/10004
摘要： Shape features in natural images influence emotions aroused in human beings. An in-depth statistical analysis helps to understand the relationship between shapes and emotions. Through experimental results on the International Affective Picture System (IAPS) dataset, evidence is presented as to the significance of roundness-angularity and simplicity-complexity on predicting emotional content in images. Shape features are combined with other state-of-the-art features to show a gain in prediction and classification accuracy. Emotions are modeled from a dimensional perspective in order to predict valence and arousal ratings, which have advantages over modeling the traditional discrete emotional categories. Images are distinguished vis-a-vis strong emotional content from emotionally neutral images with high accuracy. All of the methods and steps disclosed herein are implemented on a programmed digital computer, which may be a stand-alone machine or integrated into another piece of equipment such as a digital still or video camera including, in all embodiments, portable devices such as smart phones.
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公开(公告)号：US10043099B2
公开(公告)日：2018-08-07
申请号：US15868310
申请日：2018-01-11
发明人： James Z. Wang , Xin Lu , Poonam Suryanarayan , Reginald B. Adams , Jia Li , Michelle Newman
摘要： Shape features in natural images influence emotions aroused in human beings. An in-depth statistical analysis helps to understand the relationship between shapes and emotions. Through experimental results on the International Affective Picture System (IAPS) dataset, evidence is presented as to the significance of roundness-angularity and simplicity-complexity on predicting emotional content in images. Shape features are combined with other state-of-the-art features to show a gain in prediction and classification accuracy. Emotions are modeled from a dimensional perspective in order to predict valence and arousal ratings, which have advantages over modeling the traditional discrete emotional categories. Images are distinguished vis-a-vis strong emotional content from emotionally neutral images with high accuracy. All of the methods and steps disclosed herein are implemented on a programmed digital computer, which may be a stand-alone machine or integrated into another piece of equipment such as a digital still or video camera including, in all embodiments, portable devices such as smart phones.
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公开(公告)号：US20180150719A1
公开(公告)日：2018-05-31
申请号：US15868310
申请日：2018-01-11
发明人： James Z. Wang , Xin Lu , Poonam Suryanarayan , Reginald B. Adams , Jia Li , Michelle Newman
CPC分类号： G06K9/4671 , G06F17/30259 , G06F17/30268 , G06K9/00302 , G06K9/00684 , G06K9/00711 , G06K9/46 , G06K9/4604 , G06K9/52 , G06K9/6267 , G06T7/60 , G06T11/206 , G06T2207/10004
摘要： Shape features in natural images influence emotions aroused in human beings. An in-depth statistical analysis helps to understand the relationship between shapes and emotions. Through experimental results on the International Affective Picture System (IAPS) dataset, evidence is presented as to the significance of roundness-angularity and simplicity-complexity on predicting emotional content in images. Shape features are combined with other state-of-the-art features to show a gain in prediction and classification accuracy. Emotions are modeled from a dimensional perspective in order to predict valence and arousal ratings, which have advantages over modeling the traditional discrete emotional categories. Images are distinguished vis-a-vis strong emotional content from emotionally neutral images with high accuracy. All of the methods and steps disclosed herein are implemented on a programmed digital computer, which may be a stand-alone machine or integrated into another piece of equipment such as a digital still or video camera including, in all embodiments, portable devices such as smart phones.
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4.发明授权公开(公告)号：US09558425B2
公开(公告)日：2017-01-31
申请号：US14809499
申请日：2015-07-27
发明人： James Z. Wang , Xin Lu , Poonam Suryanarayan , Reginald B. Adams, Jr. , Jia Li , Michelle Newman
CPC分类号： G06K9/4671 , G06F17/30259 , G06F17/30268 , G06K9/00302 , G06K9/00684 , G06K9/00711 , G06K9/46 , G06K9/4604 , G06K9/52 , G06K9/6267 , G06T7/60 , G06T11/206 , G06T2207/10004
摘要： Shape features in natural images influence emotions aroused in human beings. An in-depth statistical analysis helps to understand the relationship between shapes and emotions. Through experimental results on the International Affective Picture System (IAPS) dataset, evidence is presented as to the significance of roundness-angularity and simplicity-complexity on predicting emotional content in images. Shape features are combined with other state-of-the-art features to show a gain in prediction and classification accuracy. Emotions are modeled from a dimensional perspective in order to predict valence and arousal ratings, which have advantages over modeling the traditional discrete emotional categories. Images are distinguished vis-a-vis strong emotional content from emotionally neutral images with high accuracy. All of the methods and steps disclosed herein are implemented on a programmed digital computer, which may be a stand-alone machine or integrated into another piece of equipment such as a digital still or video camera including, in all embodiments, portable devices such as smart phones.
摘要翻译： 自然形象的形状特征影响人类的情绪。 深入的统计分析有助于了解形状和情感之间的关系。 通过国际情感图像系统（IAPS）数据集的实验结果，提出了圆度 - 角度和简单复杂度对预测图像情感内容的意义。 形状特征与其他最先进的特征相结合，以显示预测和分类精度的增益。 情绪从尺寸的角度进行建模，以便预测价值和觉醒评分，这些评分具有优于传统离散情感类别建模的优点。 图像与情感中性图像具有高精度的强烈情感内容相区分。 本文公开的所有方法和步骤在编程的数字计算机上实现，该程序数字计算机可以是独立机器或集成到另一个设备中，例如数字静态或摄像机，在所有实施例中包括诸如智能的便携式设备 手机。
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5.发明申请AUTOMATICALLY COMPUTING EMOTIONS AROUSED FROM IMAGES THROUGH SHAPE MODELING 审中-公开通过形状建模从图像中自动计算出的情绪公开(公告)号：US20140049546A1
公开(公告)日：2014-02-20
申请号：US13963039
申请日：2013-08-09
发明人： James Z. Wang , Xin Lu , Poonam Suryanarayan , Reginald B. Adams, JR. , Jia Li , Michelle Newman
CPC分类号： G06K9/00302 , G06F16/5854 , G06K9/46 , G06T11/20
摘要： Shape features in natural images influence emotions aroused in human beings. An in-depth statistical analysis helps to understand the relationship between shapes and emotions. Through experimental results on the International Affective Picture System (IAPS) dataset, evidence is presented as to the significance of roundness-angularity and simplicity-complexity on predicting emotional content in images. Shape features are combined with other state-of-the-art features to show a gain in prediction and classification accuracy. Emotions are modeled from a dimensional perspective in order to predict valence and arousal ratings, which have advantages over modeling the traditional discrete emotional categories. Images are distinguished vis-a-vis strong emotional content from emotionally neutral images with high accuracy.
摘要翻译： 自然形象的形状特征影响人类的情绪。 深入的统计分析有助于了解形状和情感之间的关系。 通过国际情感图像系统（IAPS）数据集的实验结果，提出了圆度 - 角度和简单复杂度对预测图像情感内容的意义。 形状特征与其他最先进的特征相结合，以显示预测和分类精度的增益。 情绪从尺寸的角度进行建模，以便预测价值和觉醒评分，这些评分具有优于传统离散情感类别建模的优点。 图像与情感中性图像具有高精度的强烈情感内容相区分。
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公开(公告)号：US09904869B2
公开(公告)日：2018-02-27
申请号：US15395331
申请日：2016-12-30
发明人： James Z. Wang , Xin Lu , Poonam Suryanarayan , Reginald B. Adams, Jr. , Jia Li , Michelle Newman
CPC分类号： G06K9/4671 , G06F17/30259 , G06F17/30268 , G06K9/00302 , G06K9/00684 , G06K9/00711 , G06K9/46 , G06K9/4604 , G06K9/52 , G06K9/6267 , G06T7/60 , G06T11/206 , G06T2207/10004
摘要： Shape features in natural images influence emotions aroused in human beings. An in-depth statistical analysis helps to understand the relationship between shapes and emotions. Through experimental results on the International Affective Picture System (IAPS) dataset, evidence is presented as to the significance of roundness-angularity and simplicity-complexity on predicting emotional content in images. Shape features are combined with other state-of-the-art features to show a gain in prediction and classification accuracy. Emotions are modeled from a dimensional perspective in order to predict valence and arousal ratings, which have advantages over modeling the traditional discrete emotional categories. Images are distinguished vis-a-vis strong emotional content from emotionally neutral images with high accuracy. All of the methods and steps disclosed herein are implemented on a programmed digital computer, which may be a stand-alone machine or integrated into another piece of equipment such as a digital still or video camera including, in all embodiments, portable devices such as smart phones.
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7.发明申请公开(公告)号：US20150332118A1
公开(公告)日：2015-11-19
申请号：US14809499
申请日：2015-07-27
发明人： James Z. Wang , Xin Lu , Poonam Suryanarayan , Reginald B. Adams, JR. , Jia Li , Michelle Newman
CPC分类号： G06K9/4671 , G06F17/30259 , G06F17/30268 , G06K9/00302 , G06K9/00684 , G06K9/00711 , G06K9/46 , G06K9/4604 , G06K9/52 , G06K9/6267 , G06T7/60 , G06T11/206 , G06T2207/10004
摘要： Shape features in natural images influence emotions aroused in human beings. An in-depth statistical analysis helps to understand the relationship between shapes and emotions. Through experimental results on the International Affective Picture System (IAPS) dataset, evidence is presented as to the significance of roundness-angularity and simplicity-complexity on predicting emotional content in images. Shape features are combined with other state-of-the-art features to show a gain in prediction and classification accuracy. Emotions are modeled from a dimensional perspective in order to predict valence and arousal ratings, which have advantages over modeling the traditional discrete emotional categories. Images are distinguished vis-a-vis strong emotional content from emotionally neutral images with high accuracy. All of the methods and steps disclosed herein are implemented on a programmed digital computer, which may be a stand-alone machine or integrated into another piece of equipment such as a digital still or video camera including, in all embodiments, portable devices such as smart phones.
摘要翻译： 自然形象的形状特征影响人类的情绪。 深入的统计分析有助于了解形状和情感之间的关系。 通过国际情感图像系统（IAPS）数据集的实验结果，提出了圆度 - 角度和简单复杂度对预测图像情感内容的意义。 形状特征与其他最先进的特征相结合，以显示预测和分类精度的增益。 情绪从尺寸的角度进行建模，以便预测价值和觉醒评分，这些评分具有优于传统离散情感类别建模的优点。 图像与情感中性图像具有高精度的强烈情感内容相区分。 本文公开的所有方法和步骤在编程的数字计算机上实现，该程序数字计算机可以是独立机器或集成到另一个设备中，例如数字静态或摄像机，在所有实施例中包括诸如智能的便携式设备 手机。
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