Various researches have been performed with video abstraction with the constant development of multimedia technology. However, there are some deficiencies that have been encountered in the pre-processing of video frames before attaining classified video archives. To overcome the drawbacks in pre-processing, feature extraction and classification approaches are considered. Here, video indexing has been anticipated with several features’ extraction with dominant frame generation for the input video frame. Fuzzy-based SVM classifier is utilized to categorize frame set into dominant structures. Multi-dimensional Histogram of Oriented Gradients (HOG) and colour feature extraction are used to extract texture features from the video frame. Using the frame sequence, the vector space of structures is captured; dominant frameworks are utilized in video indexing. Shot transitions’ classification is done with a fuzzy system. Experimental outcomes demonstrate that shot boundary detection accuracy increases with an increase in iterations. The simulation was carried out in MATLAB environment. This technique attains an accuracy of about 95.4%, the precision of 100%, and the F1 score of 100% and a recall of 100%. The misclassification rate is 4.6%. The proposed method shows better trade-off than the existing techniques.

中文翻译：

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改进的基于特征提取的基于模糊的SVM分类系统，用于视频索引和检索

随着多媒体技术的不断发展，已经对视频抽象进行了各种研究。但是，在获得分类的视频档案之前，对视频帧进行预处理时会遇到一些缺陷。为了克服预处理中的缺点，考虑了特征提取和分类方法。在此，已经期望通过对输入视频帧进行占优势的帧生成和多个特征提取来实现视频索引。基于模糊的SVM分类器用于将框架集分类为主要结构。定向梯度的多维直方图（HOG）和颜色特征提取用于从视频帧中提取纹理特征。使用帧序列，捕获结构的向量空间；视频索引中使用了主要框架。镜头过渡的分类是通过模糊系统完成的。实验结果表明，镜头边界检测精度随着迭代次数的增加而增加。仿真是在MATLAB环境下进行的。此技术的准确度约为95.4％，准确度为100％，F1得分为100％，召回率为100％。错误分类率为4.6％。所提出的方法显示出比现有技术更好的折衷。F1分数为100％，召回率为100％。错误分类率为4.6％。所提出的方法显示出比现有技术更好的折衷。F1分数为100％，召回率为100％。错误分类率为4.6％。所提出的方法显示出比现有技术更好的折衷。