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首页> 外文期刊>Sensors Journal, IEEE >Multiaxial Haar-Like Feature and Compact Cascaded Classifier for Versatile Recognition
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Multiaxial Haar-Like Feature and Compact Cascaded Classifier for Versatile Recognition

机译：多轴Haar-Like特征和紧凑级联分类器，用于多功能识别

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摘要

A versatile recognition algorithm has been proposed to process image, sound, and 3-D acceleration signals with a common framework at low calculation cost. Firstly, a novel 1-D Haar-like feature is used to roughly extract frequency information from temporal signals. Biaxial and mean-embedded Haar-like features are proposed to extract the standard deviation and the interaxial correlation from 3-D acceleration signals. Secondly, two techniques are proposed to build a compact cascaded classifier. Redundant feature selection (RFS) incorporates the features which are already selected in previous stage classifiers to reduce the calculation cost. A dynamic look-up table (DLUT) is proposed to construct a look-up table-based weak classifier with the smallest possible number of bins. A train loss function is by globally optimized using dynamic programming. The proposed algorithm is tested experimentally on speechonspeech classification and human activity recognition. The proposed algorithm yields a speechonspeech classification performance comparable to the state-of-art method called MFCC while reducing the calculation cost by 100 times. The algorithm also achieves human activity recognition accuracy of 96.1% with calculation cost reduction of 84% compared with the state-of-art method based on C4.5 decision-tree classifier using the basic statistical features. The proposed algorithm has been employed to build the versatile recognition processor.
机译：已经提出了一种通用的识别算法，该算法以较低的计算成本以通用框架处理图像，声音和3D加速度信号。首先，一种新颖的一维类似Haar的特征用于从时间信号中大致提取频率信息。提出了双轴和均值嵌入的类似Haar的特征，以从3-D加速度信号中提取标准偏差和轴间相关性。其次，提出了两种技术来构建紧凑的级联分类器。冗余特征选择（RFS）合并了前阶段分类器中已经选择的特征，以减少计算成本。提出了一种动态查找表（DLUT），以构建具有尽可能少的bin数量的基于查找表的弱分类器。通过使用动态编程来全局优化列车损失功能。对语音/非语音分类和人类活动识别进行了实验测试。提出的算法产生的语音/非语音分类性能与称为MFCC的最新方法相当，同时将计算成本降低了100倍。与基于C4.5决策树分类器并使用基本统计特征的最新方法相比，该算法还实现了96.1％的人类活动识别精度，计算成本降低了84％。所提出的算法已被用来构建通用识别处理器。

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