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Indoor versus outdoor scene recognition for navigation of a micro aerial vehicle using spatial color gist wavelet descriptors
Visual Computing for Industry, Biomedicine, and Art Pub Date : 2019-11-26 , DOI: 10.1186/s42492-019-0030-9
Anitha Ganesan 1 , Anbarasu Balasubramanian 1
Affiliation  

In the context of improved navigation for micro aerial vehicles, a new scene recognition visual descriptor, called spatial color gist wavelet descriptor (SCGWD), is proposed. SCGWD was developed by combining proposed Ohta color-GIST wavelet descriptors with census transform histogram (CENTRIST) spatial pyramid representation descriptors for categorizing indoor versus outdoor scenes. A binary and multiclass support vector machine (SVM) classifier with linear and non-linear kernels was used to classify indoor versus outdoor scenes and indoor scenes, respectively. In this paper, we have also discussed the feature extraction methodology of several, state-of-the-art visual descriptors, and four proposed visual descriptors (Ohta color-GIST descriptors, Ohta color-GIST wavelet descriptors, enhanced Ohta color histogram descriptors, and SCGWDs), in terms of experimental perspectives. The proposed enhanced Ohta color histogram descriptors, Ohta color-GIST descriptors, Ohta color-GIST wavelet descriptors, SCGWD, and state-of-the-art visual descriptors were evaluated, using the Indian Institute of Technology Madras Scene Classification Image Database two, an Indoor-Outdoor Dataset, and the Massachusetts Institute of Technology indoor scene classification dataset [(MIT)-67]. Experimental results showed that the indoor versus outdoor scene recognition algorithm, employing SVM with SCGWDs, produced the highest classification rates (CRs)—95.48% and 99.82% using radial basis function kernel (RBF) kernel and 95.29% and 99.45% using linear kernel for the IITM SCID2 and Indoor-Outdoor datasets, respectively. The lowest CRs—2.08% and 4.92%, respectively—were obtained when RBF and linear kernels were used with the MIT-67 dataset. In addition, higher CRs, precision, recall, and area under the receiver operating characteristic curve values were obtained for the proposed SCGWDs, in comparison with state-of-the-art visual descriptors.

中文翻译:

使用空间颜色要点小波描述子的室内与室外场景识别,用于微型飞机的导航

在改进的微型飞行器导航的背景下,提出了一种新的场景识别视觉描述符,称为空间色域小波描述符(SCGWD)。SCGWD是通过将建议的Ohta color-GIST小波描述符与普查变换直方图(CENTRIST)空间金字塔表示描述符相结合而开发的,用于对室内和室外场景进行分类。使用具有线性和非线性内核的二进制和多类支持向量机(SVM)分类器分别对室内和室外场景以及室内场景进行分类。在本文中,我们还讨论了几种最先进的视觉描述符和四种拟议的视觉描述符(Ohta color-GIST描述符,Ohta color-GIST小波描述符,增强型Ohta颜色直方图描述符,和SCGWD),从实验角度来讲。拟议的增强型Ohta颜色直方图描述符,Ohta颜色GIST描述符,Ohta颜色GIST小波描述符,SCGWD和最先进的视觉描述符使用印度理工学院Madras场景分类图像数据库进行了评估,其中两个室内-室外数据集和麻省理工学院室内场景分类数据集[(MIT)-67]。实验结果表明,使用带有SCGWD的SVM的室内与室外场景识别算法,使用径向基函数核(RBF)核的分类率(CR)最高,分别为95.48%和99.82%,使用线性核的核分类率分别为95.29%和99.45% IITM SCID2和室内室外数据集。CR最低:2.08%和4.92%,分别是将RBF和线性核与MIT-67数据集一起使用时获得的。此外,与最新的视觉描述符相比,针对拟议的SCGWD获得了更高的CR,精度,召回率和接收器工作特性曲线值下的面积。
更新日期:2019-11-26
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