Print Email Facebook Twitter Image-based Video Search Engine: Keyframe Extraction Title Image-based Video Search Engine: Keyframe Extraction Author Bos, Robert (TU Delft Electrical Engineering, Mathematics and Computer Science) Zheng, Leo (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Dauwels, J.H.G. (mentor) Llombart, Nuria (graduation committee) Bol, E.W. (graduation committee) Degree granting institution Delft University of Technology Programme Electrical Engineering Project Bachelor graduation project Electrical Engineering Date 2022-06-20 Abstract In this report, the analysis and design of a system that extracts keyframes from videos is detailed. The need for such a sub-module stems from the similarity of frames in a video. To aid in reducing the computation time of the content based video search engine, the Keyframe Extraction Module reduces the amount of frames by discarding frames that are similar in information. Determining what frames can be considered similar is one of the main challenges, as there are many ways of assigning values to how much frames differ. In the past decades, many research has been done on keyframe extraction and video summarization and many methods are proposed to form keyframe selections, varying in what is considered salient information and varying in computation time. The most challenging part of the design is that there is a time constraint present, which called for a proper analysis in what methods are suitable. After all, this limitation when creating video summaries is often not a large topic in research papers.This report will cover Shot Detection techniques along with various Keyframe Extraction methods that can be categorized in clustering, visual content, fixed selection and uniform sampling. Furthermore, evaluation methods like the Fidelity measure for the performance of particular methods are also addressed, as determining how well a keyframe selection is is not trivial. It is concluded that out of the techniques analyzed, a combination of VSUMM clustering and histogram matching along with histogram-based shot based detection with a CFAR threshold and pre-sampling is most suitable for the general case under a time constraint. Future work could include looking at hierarchical clustering methods and optimizing the Shot Boundary Detection module following the most recent papers. Subject Image RetrievalVideo searchKeyframe ExtractionShot based detectionVideo summarizationVSUMM To reference this document use: http://resolver.tudelft.nl/uuid:1f2f8fdc-dca9-46e0-a669-1d4f7c5170a5 Part of collection Student theses Document type bachelor thesis Rights © 2022 Robert Bos, Leo Zheng Files PDF BAP_Keyframe_Extraction_1_.pdf 6.5 MB Close viewer /islandora/object/uuid:1f2f8fdc-dca9-46e0-a669-1d4f7c5170a5/datastream/OBJ/view