Compare Between Histogram Similarity and Histogram Differencing For More Brief Key Frames Extraction from Video Stream

Abstract

Speedy increase in the employ of the content of multimedia is very commonly perceived in existing generations. Video is one of the extreme exceedingly recovered data of multimedia which can provide quick fixes and immediate gratification for whole the kinds of user information willingness. Detection of Key frame from Videos and classification of videos are the necessary protocols in the complements tasks of video retrieval. The aim of this research is to compare between histogram similarity and histogram differencing for more brief key frames extraction from video stream. The suggested system for key frame extraction has three steps. Firstly, the video frames series will take and the characteristics for points of interest are elicited utilizing SUSAN detector. After eliciting interest characteristics from all video frames images, secondly, K-Means clustering technique was utilized for these features to construct the clusters with a number of interest points. Thirdly, a histogram builds for each video frame based on numbers of features in each cluster. X-axis of a histogram represents the cluster number and y-axis represents the number of features in each cluster. For key frames extraction, numbers for each cluster can be offered and a query histogram can be constructed based on entered clusters’ numbers. A query histogram was matched with every video frame histogram using Manhattan distance to discover the similar histogram to query histogram. After chosen similar histogram, it can extract all key frames from that video frames. The experimental results show that the number of key frames extracted from the video is very brief when using the histogram similarity compared to the histogram differencing. For example, the number of key frames extracted from a car video is 70 when using the histogram similarity, while it is 180 when using the histogram differencing.

A Comparison between Backpropagation Neural Network and Seven Moments for More Accurate Fingerprint Video Frames Recognition

“…In the case when a pixel mask's brightness is put to comparison with the brightness of a nucleus, the region of the mask will be built using the nucleus' brightness as a benchmark. The region of the mask in this case is denoted by (USAN), and it could be derived from (Univalue Segment Assimilating Nucleus) 10 . Each mask's nucleus and pixel contain a function that is used to evoke a corner.…”

Multimodal video abstraction into a static document using deep learning

“…The most straightforward technique is to use the first, middle, or last frame as a keyframe. However, one disadvantage of these approaches is that the recovered keyframes may not be capable of portraying the video material [26]- [34].…”