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A Novel Feature Extraction Method for Scene Recognition Based on Centered Convolutional Restricted Boltzmann Machines

机译:一种新的基于maTLaB的场景识别特征提取方法   中心卷积限制玻尔兹曼机器

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摘要

Scene recognition is an important research topic in computer vision, whilefeature extraction is a key step of object recognition. Although classicalRestricted Boltzmann machines (RBM) can efficiently represent complicated data,it is hard to handle large images due to its complexity in computation. In thispaper, a novel feature extraction method, named Centered ConvolutionalRestricted Boltzmann Machines (CCRBM), is proposed for scene recognition. Theproposed model is an improved Convolutional Restricted Boltzmann Machines(CRBM) by introducing centered factors in its learning strategy to reduce thesource of instabilities. First, the visible units of the network are redefinedusing centered factors. Then, the hidden units are learned with a modifiedenergy function by utilizing a distribution function, and the visible units arereconstructed using the learned hidden units. In order to achieve bettergenerative ability, the Centered Convolutional Deep Belief Networks (CCDBN) istrained in a greedy layer-wise way. Finally, a softmax regression isincorporated for scene recognition. Extensive experimental evaluations usingnatural scenes, MIT-indoor scenes, and Caltech 101 datasets show that theproposed approach performs better than other counterparts in terms ofstability, generalization, and discrimination. The CCDBN model is more suitablefor natural scene image recognition by virtue of convolutional property.
机译:场景识别是计算机视觉中的重要研究课题,而特征提取是对象识别的关键步骤。尽管经典的受限玻尔兹曼机(RBM)可以有效地表示复杂的数据,但由于其计算复杂性,因此难以处理大图像。本文提出了一种新的特征提取方法,称为中心卷积受限玻尔兹曼机(CCRBM),用于场景识别。所提出的模型是一种改进的卷积受限玻尔兹曼机(CRBM),它在其学习策略中引入了中心因素,以减少不稳定性的来源。首先,使用中心因素重新定义网络的可见单位。然后,利用分布函数利用改进的能量函数学习隐藏单元,并使用学习到的隐藏单元重建可见单元。为了获得更好的生成能力,对中心卷积深信度网络(CCDBN)进行了贪婪分层训练。最后,结合softmax回归进行场景识别。使用自然场景,室内MIT场景和Caltech 101数据集进行的广泛实验评估表明,在稳定性,泛化性和区分性方面,所提出的方法比其他方法表现更好。 CCDBN模型具有卷积特性,因此更适合自然场景图像的识别。

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