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Sublinear time classification via feature padding and hashing
Sublinear time classification via feature padding and hashing
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机译:通过特征填充和哈希进行亚线性时间分类
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摘要
A linear function describing a framework for identifying an object of class k in an image sample x may be described by: wk*x+bk, where bk is the bias term. The higher the value obtained for a particular classifier, the better the match or strength of identity. A method is disclosed for classifier and/or content padding to convert dot-products to distances, applying a hashing and/or nearest neighbor technique on the resulting padded vectors, and preprocessing that may improve the hash entropy. A vector for an image, an audio, and/or a video may be received. One or more classifier vectors may be obtained. A padded image, video, and/or audio vector and classifier vector may be generated. A dot product may be approximated and a hashing and/or nearest neighbor technique may be performed on the approximated dot product to identify at least one class (or object) present in the image, video, and/or audio.
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机译:可以通过以下方式描述描述用于识别图像样本x中类别k的对象的框架的线性函数:w k Sub> * x + b k Sub>,其中b k Sub>是偏差项。从特定分类器获得的值越高,匹配或标识的强度就越好。公开了一种用于分类器和/或内容填充以将点乘积转换为距离,对所生成的填充向量应用哈希和/或最近邻居技术以及可以改善哈希熵的预处理的方法。可以接收图像,音频和/或视频的矢量。可以获得一个或多个分类器矢量。可以生成填充的图像,视频和/或音频矢量和分类器矢量。可以近似点积,并且可以对近似点积执行散列和/或最近邻技术,以识别图像,视频和/或音频中存在的至少一个类别(或对象)。
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