| Summary: | 碩士 === 國立臺中科技大學 === 資訊工程系碩士班 === 100 === In our thesis, we present different key feature sets to describe the content of every shot in video. Calculate the distance between query image (or query video) and video database by distance measure of feature. Return the most similar results to build a multi-video retrieval function.
In our study, we analyze the digital videos on the Internet. By K-mean algorithm and the gradient histogram of LBP, we respectively transferred them into color and texture features and extracted histogram of each frame. According to the features, the distance between continuous frames can be calculated and the shot boundary can be detected to identify the shot boundaries of the video. Thus, different shot can be created. After video were cut into representative shots, filter and cluster the shots by video analysis function, so that each shot can be more representative. According to the correct shots, key feature set can be built to describe the video. Finally we can find out the similar results by calculating the distance between query video and video database. After the steps above, the goal of multi-video retrieval can be reached and the query result can provide the user with a different way of video retrieval.
Where the video retrieval method proposed in this study differs from existing video retrieval is that the current high visibility online video sharing web sites such as YouTube, Yahoo video, VEVO and others are all text-based queries. The query function of our study is based on the color and texture features of the key feature set. These retrieval results differ from using words to describe the video. Instead, it searches with color and content of texture in video. Thus, chaos of the result caused by different text reading method can be avoided. The users can be more intuitive to query the video interested by using the same image or video.
In the experimental results, we will focus on detecting the correct shot transferring based on shot boundary respectively. By shot filter and shot cluster function in video analysis, we identified the difference of shot content, so that the key feature set can be more representative. Finally experimental result in video retrieval, it can be divided into two ways - the image query videos and the video query videos to return similar result of the query goals. From the results we know similar shot of the query target can be found simply by sections of video.
|
|---|
