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161 条记录 (耗时 0.034 秒)

    Generating visually representative video thumbnails
    23.
    发明授权
    Generating visually representative video thumbnails 有权
    生成视觉上具有代表性的视频缩略图

    公开(公告)号：US07212666B2

    公开(公告)日：2007-05-01

    申请号：US10405971

    申请日：2003-04-01

    申请人： Dong Zhang ,  Yijin Wang ,  Hong-Jiang Zhang

    发明人： Dong Zhang ,  Yijin Wang ,  Hong-Jiang Zhang

    IPC分类号： G06K9/00

    CPC分类号： G06K9/00711 ,  G06F17/30843 ,  G06K9/6255 ,  G11B27/102 ,  G11B27/28

    摘要： An algorithm identifies a salient video frame from a video sequence for use as a video thumbnail. The identification of a video thumbnail is based on a frame goodness measure. The algorithm calculates a color histogram of a frame, and then calculates the entropy and standard deviation of the color histogram. The frame goodness measure is a weighted combination of the entropy and the standard deviation. A video frame having the highest value of frame goodness measure for a video sequence is determined as the video thumbnail for a video sequence.

    摘要翻译： 算法从视频序列中识别显着的视频帧，以用作视频缩略图。 视频缩略图的识别基于帧良品度量。 该算法计算一个帧的颜色直方图，然后计算颜色直方图的熵和标准偏差。 框架良好度量是熵和标准偏差的加权组合。 确定视频序列具有最高的帧质量度量值的视频帧作为视频序列的视频缩略图。

    Relevance Maximizing, Iteration Minimizing, Relevance-Feedback, Content-Based Image Retrieval (CBIR)
    24.
    发明申请
    Relevance Maximizing, Iteration Minimizing, Relevance-Feedback, Content-Based Image Retrieval (CBIR) 有权
    相关性最大化，迭代最小化，相关性反馈，基于内容的图像检索（CBIR）

    公开(公告)号：US20060248044A1

    公开(公告)日：2006-11-02

    申请号：US11458057

    申请日：2006-07-17

    申请人： Hong-Jiang Zhang ,  Zhong Su ,  Xingquan Zhu

    发明人： Hong-Jiang Zhang ,  Zhong Su ,  Xingquan Zhu

    IPC分类号： G06F17/30

    CPC分类号： G06F17/30256 ,  G06K9/6215 ,  G06K9/6253 ,  G06K9/6263 ,  G06K9/6278 ,  Y10S707/99931 ,  Y10S707/99932 ,  Y10S707/99933 ,  Y10S707/99934 ,  Y10S707/99935 ,  Y10S707/99936 ,  Y10S707/99943 ,  Y10S707/99945 ,  Y10S707/99948

    摘要： An implementation of a technology, described herein, for relevance-feedback, content-based image retrieval minimizes the number of iterations for user feedback regarding the semantic relevance of exemplary images while maximizing the resulting relevance of each iteration. One technique for accomplishing this is to use a Bayesian classifier to treat positive and negative feedback examples with different strategies. In addition, query refinement techniques are applied to pinpoint the users' intended queries with respect to their feedbacks. These techniques further enhance the accuracy and usability of relevance feedback. This abstract itself is not intended to limit the scope of this patent. The scope of the present invention is pointed out in the appending claims.

    摘要翻译： 这里描述的用于相关性反馈的基于内容的图像检索的技术的实现使得关于示例性图像的语义相关性的用户反馈的迭代次数最小化，同时最大化每次迭代的结果相关性。 实现这一点的一种技术是使用贝叶斯分类器来处理具有不同策略的正反馈示例。 另外，使用查询优化技术来针对用户对其反馈的预期查询进行定位。 这些技术进一步提高了相关性反馈的准确性和可用性。 本摘要本身并不旨在限制本专利的范围。 在所附权利要求中指出了本发明的范围。

    Pose-invariant face recognition system and process
    25.
    发明授权
    Pose-invariant face recognition system and process 有权

    公开(公告)号：US07127087B2

    公开(公告)日：2006-10-24

    申请号：US10983194

    申请日：2004-11-05

    申请人： Fu Jie Huang ,  Hong-Jiang Zhang ,  Tsuhan Chen

    发明人： Fu Jie Huang ,  Hong-Jiang Zhang ,  Tsuhan Chen

    IPC分类号： G06K9/00 ,  G06K9/62

    CPC分类号： G06K9/00228 ,  G06K9/00288 ,  G06K9/6292

    摘要： A face recognition system and process for identifying a person depicted in an input image and their face pose. This system and process entails locating and extracting face regions belonging to known people from a set of model images, and determining the face pose for each of the face regions extracted. All the extracted face regions are preprocessed by normalizing, cropping, categorizing and finally abstracting them. More specifically, the images are normalized and cropped to show only a persons face, categorized according to the face pose of the depicted person's face by assigning them to one of a series of face pose ranges, and abstracted preferably via an eigenface approach. The preprocessed face images are preferably used to train a neural network ensemble having a first stage made up of a bank of face recognition neural networks each of which is dedicated to a particular pose range, and a second stage constituting a single fusing neural network that is used to combine the outputs from each of the first stage neural networks. Once trained, the input of a face region which has been extracted from an input image and preprocessed (i.e., normalized, cropped and abstracted) will cause just one of the output units of the fusing portion of the neural network ensemble to become active. The active output unit indicates either the identify of the person whose face was extracted from the input image and the associated face pose, or that the identity of the person is unknown to the system.

    Automatic music mood detection
    26.
    发明授权
    Automatic music mood detection 有权
    自动音乐心情检测

    公开(公告)号：US07115808B2

    公开(公告)日：2006-10-03

    申请号：US11265685

    申请日：2005-11-02

    申请人： Lie Lu ,  Hong-Jiang Zhang

    发明人： Lie Lu ,  Hong-Jiang Zhang

    IPC分类号： G10H1/40 ,  G10H7/00 ,  G06F17/00

    CPC分类号： G10H1/00 ,  G10H2210/071 ,  G10H2240/085

    摘要： A system and methods use music features extracted from music to detect a music mood within a hierarchical mood detection framework. A two-dimensional mood model divides music into four moods which include contentment, depression, exuberance, and anxious/frantic. A mood detection algorithm uses a hierarchical mood detection framework to determine which of the four moods is associated with a music clip based on the extracted features. In a first tier of the hierarchical detection process, the algorithm determines one of two mood groups to which the music clip belongs. In a second tier of the hierarchical detection process, the algorithm then determines which mood from within the selected mood group is the appropriate, exact mood for the music clip. Benefits of the mood detection system include automatic detection of music mood which can be used as music metadata to manage music through music representation and classification.

    摘要翻译： 系统和方法使用从音乐中提取的音乐特征来检测分层情绪检测框架内的音乐心情。 二维情绪模型将音乐分为四种情绪，包括满足感，抑郁症，繁荣感和焦虑/疯狂。 情绪检测算法使用分级情绪检测框架来基于提取的特征来确定四种情绪中的哪一种与音乐剪辑相关联。 在层次检测过程的第一层中，算法确定音乐剪辑所属的两个心情组之一。 在层次检测过程的第二层中，算法然后确定来自所选择的心情组中的哪个心情是音乐剪辑的适当的精确心情。 情绪检测系统的优点包括自动检测音乐心情，可用作音乐元数据，通过音乐表示和分类来管理音乐。

    Image retrieval systems and methods with semantic and feature based relevance feedback
    27.
    发明授权
    Image retrieval systems and methods with semantic and feature based relevance feedback 有权

    公开(公告)号：US07099860B1

    公开(公告)日：2006-08-29

    申请号：US09702292

    申请日：2000-10-30

    申请人： Wen-Yin Liu ,  Hong-Jiang Zhang ,  Ye Lu

    发明人： Wen-Yin Liu ,  Hong-Jiang Zhang ,  Ye Lu

    IPC分类号： G06F17/30

    CPC分类号： G06F17/30265 ,  G06F17/30256 ,  G06F19/00 ,  Y10S707/99933 ,  Y10S707/99934 ,  Y10S707/99935

    摘要： An image retrieval system performs both keyword-based and content-based image retrieval. A user interface allows a user to specify queries using a combination of keywords and examples images. Depending on the input query, the image retrieval system finds images with keywords that match the keywords in the query and/or images with similar low-level features, such as color, texture, and shape. The system ranks the images and returns them to the user. The user interface allows the user to identify images that are more relevant to the query, as well as images that are less or not relevant to the query. The user may alternatively elect to refine the search by selecting one example image from the result set and submitting its low-level features in a new query. The image retrieval system monitors the user feedback and uses it to refine any search efforts and to train itself for future search queries. In the described implementation, the image retrieval system seamlessly integrates feature-based relevance feedback and semantic-based relevance feedback.

    Beat analysis of musical signals
    28.
    发明授权
    Beat analysis of musical signals 失效
    节奏分析音乐信号

    公开(公告)号：US07026536B2

    公开(公告)日：2006-04-11

    申请号：US10811287

    申请日：2004-03-25

    申请人： Lie Lu ,  Hong-Jiang Zhang

    发明人： Lie Lu ,  Hong-Jiang Zhang

    IPC分类号： G10H7/00

    CPC分类号： G10H1/40 ,  G10H2210/076 ,  G10H2210/391 ,  G10H2240/325 ,  G10H2250/135 ,  G10H2250/285

    摘要： A system and methods analyze music to detect musical beats and to rectify beats that are out of sync with the actual beat phase of the music. The music analysis includes onset detection, tempo/meter estimation, and beat analysis, which includes the rectification of out-of-sync beats.

    摘要翻译： 系统和方法分析音乐以检测音乐节奏并纠正与音乐的实际节奏不同步的节奏。 音乐分析包括开始检测，速度/仪表估计和拍频分析，其中包括纠错不同步拍摄。

    Automatic music mood detection
    29.
    发明申请
    Automatic music mood detection 失效
    自动音乐心情检测

    公开(公告)号：US20050211071A1

    公开(公告)日：2005-09-29

    申请号：US10811281

    申请日：2004-03-25

    申请人： Lie Lu ,  Hong-Jiang Zhang

    发明人： Lie Lu ,  Hong-Jiang Zhang

    IPC分类号： G10H1/00 ,  G10H1/40 ,  G10H7/00

    CPC分类号： G10H1/00 ,  G10H2210/071 ,  G10H2240/085

    摘要： A system and methods use music features extracted from music to detect a music mood within a hierarchical mood detection framework. A two-dimensional mood model divides music into four moods which include contentment, depression, exuberance, and anxious/frantic. A mood detection algorithm uses a hierarchical mood detection framework to determine which of the four moods is associated with a music clip based on the extracted features. In a first tier of the hierarchical detection process, the algorithm determines one of two mood groups to which the music clip belongs. In a second tier of the hierarchical detection process, the algorithm then determines which mood from within the selected mood group is the appropriate, exact mood for the music clip. Benefits of the mood detection system include automatic detection of music mood which can be used as music metadata to manage music through music representation and classification.

    摘要翻译： 系统和方法使用从音乐中提取的音乐特征来检测分层情绪检测框架内的音乐心情。 二维情绪模型将音乐分为四种情绪，包括满足感，抑郁症，繁荣感和焦虑/疯狂。 情绪检测算法使用分级情绪检测框架来基于提取的特征来确定四种情绪中的哪一种与音乐剪辑相关联。 在层次检测过程的第一层中，算法确定音乐剪辑所属的两个心情组之一。 在层次检测过程的第二层中，算法然后确定来自所选择的心情组中的哪个心情是音乐剪辑的适当的精确心情。 情绪检测系统的优点包括自动检测音乐心情，可用作音乐元数据，通过音乐表示和分类来管理音乐。