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    • 2. 发明授权
    • Method and system for entropy-based semantic hashing
    • 基于熵的语义散列的方法和系统
    • US08676725B1
    • 2014-03-18
    • US12794380
    • 2010-06-04
    • Ruei-Sung LinDavid RossJay Yagnik
    • Ruei-Sung LinDavid RossJay Yagnik
    • G06F15/18
    • G06N99/005
    • Methods, systems and articles of manufacture for identifying semantic nearest neighbors in a feature space are described herein. A method embodiment includes generating an affinity matrix for objects in a given feature space, wherein the affinity matrix identifies the semantic similarity between each pair of objects in the feature space, training a multi-bit hash function using a greedy algorithm that increases the Hamming distance between dissimilar objects in the feature space while minimizing the Hamming distance between similar objects, and identifying semantic nearest neighbors for an object in a second feature space using the multi-bit hash function. A system embodiment includes a hash generator configured to generate the affinity matrix and train the multi-bit hash function, and a similarity determiner configured to identify semantic nearest neighbors for an object in a second feature space using the multi-bit hash function.
    • 本文描述了用于识别特征空间中的语义最近邻居的方法,系统和制品。 方法实施例包括为给定特征空间中的对象生成亲和度矩阵,其中亲和矩阵识别特征空间中每对对象之间的语义相似性,使用增加汉明距离的贪心算法训练多比特哈希函数 在特征空间中的不相似对象之间,同时使相似对象之间的汉明距离最小化,并且使用多位哈希函数来识别第二特征空间中的对象的语义最近邻居。 系统实施例包括被配置为生成亲和度矩阵并训练多比特哈希函数的哈希发生器,以及被配置为使用多比特哈希函数来识别第二特征空间中的对象的语义最近邻居的相似性确定器。
    • 3. 发明授权
    • Matching based upon rank
    • 基于等级匹配
    • US08805090B1
    • 2014-08-12
    • US13368317
    • 2012-02-07
    • Jay YagnikSergey Ioffe
    • Jay YagnikSergey Ioffe
    • G06K9/68
    • G06K9/6212
    • Systems and methods for measuring consistency between two objects based upon a rank of object elements instead of based upon the values of those object elements. Objects being compared can be represented by d-dimension feature vectors, U and V, where each dimension includes an associated value. U and V can be converted to rank vectors, P and Q, where values of U and V dimensions are replaced by an ordered rank or a function thereof. Analysis directed to the consistency between U and V can be accomplished by determining consistency between P and Q, which can be more efficient and more accurate, particularly with regard to illumination-invariant comparisons.
    • 基于对象元素的等级而不是基于这些对象元素的值来测量两个对象之间的一致性的系统和方法。 被比较的对象可以由d维特征向量U和V表示,其中每个维度包括相关联的值。 U和V可以被转换为等级向量P和Q,其中U和V维度的值被有序等级或其功能所代替。 可以通过确定P和Q之间的一致性来实现对U和V之间的一致性的分析,这可以更有效和更准确,特别是在照明不变比较方面。
    • 4. 发明授权
    • Detection and classification of matches between time-based media
    • 基于时间的媒体之间的匹配检测和分类
    • US08238669B2
    • 2012-08-07
    • US12174366
    • 2008-07-16
    • Michele CovellJay YagnikJeff FaustShumeet Baluja
    • Michele CovellJay YagnikJeff FaustShumeet Baluja
    • G06K9/62H04N7/10H04N7/025
    • G06K9/00758G06F17/30784
    • A system and method detects matches between portions of video content. A matching module receives an input video fingerprint representing an input video and a set of reference fingerprints representing reference videos in a reference database. The matching module compares the reference fingerprints and input fingerprints to generate a list of candidate segments from the reference video set. Each candidate segment comprises a time-localized portion of a reference video that potentially matches the input video. A classifier is applied to each of the candidate segments to classify the segment as a matching segment or a non-matching segment. A result is then outputted identifying a matching portion of a reference video from the reference video set based on the segments classified as matches.
    • 系统和方法检测视频内容的部分之间的匹配。 匹配模块接收表示参考数据库中的参考视频的输入视频和一组参考指纹的输入视频指纹。 匹配模块比较参考指纹和输入指纹,以从参考视频集中生成候选片段的列表。 每个候选片段包括潜在地匹配输入视频的参考视频的时间局部化部分。 将分类器应用于每个候选片段以将片段分类为匹配片段或非匹配片段。 然后基于被分类为匹配的段,从参考视频集中输出标识参考视频的匹配部分的结果。