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Content Based Video Retrieval in Transformed Domain using Fractional Coefficients

机译:基于分数系数的变换域中基于内容的视频检索

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摘要

With the development of multimedia and growing database there is huge demand of video retrievalsystems. Due to this, there is a shift from text based retrieval systems to content based retrievalsystems. Selection of extracted features play an important role in content based video retrieval. Goodfeatures selection also allows the time and space costs of the retrieval process to be reduced.Different methods[1,2,3] have been proposed to develop video retrievals systems to achieve betterperformance in terms of accuracy.The proposed technique uses transforms to extract the features. The used transforms are DiscreteCosine, Walsh, Haar, Kekre, Discrete Sine, Slant and Discrete Hartley transforms. The benefit ofenergy compaction of transforms in higher coefficients is taken to reduce the feature vector size bytaking fractional coefficients[5] of transformed frames of video. Smaller feature vector size results inless time for comparison of feature vectors resulting in faster retrieval of images. The feature vectorsare extracted and coefficients sets are considered as feature vectors (100%, 6.25%, 3.125%,1.5625%, 0.7813%, 0.39%, 0.195%, 0.097%, 0.048%, 0.024%, 0.012%, 0.006% and 0.003% ofcomplete transformed coefficients). The database consists of 500 videos spread across 10categories.
机译:随着多媒体的发展和数据库的增长,对视频检索系统的需求很大。因此,存在从基于文本的检索系统到基于内容的检索系统的转变。提取特征的选择在基于内容的视频检索中起着重要作用。良好的特征选择还可以减少检索过程的时间和空间成本。已经提出了各种方法[1,2,3]来开发视频检索系统,以在准确性方面实现更好的性能。所提出的技术使用变换来提取图像。特征。所使用的变换是离散余弦,沃尔什,哈尔,凯克雷,离散正弦,倾斜和离散哈特利变换。通过采用视频变换帧的分数系数[5],可以利用较高系数的变换能量压缩的优势来减少特征向量的大小。较小的特征向量大小导致没有时间进行特征向量比较,从而可以更快地检索图像。提取特征向量并将系数集视为特征向量(100%,6.25%,3.125%,1.5625%,0.7813%,0.39%,0.195%,0.097%,0.048%,0.024%,0.012%,0.006%和0.003完整转换系数的百分比)。该数据库包含10个类别的500个视频。

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