Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.631732
Title: Multimodal segmentation for data mining applications in multimedia engineering
Author: Damoni, Arben
Awarding Body: London South Bank University
Current Institution: London South Bank University
Date of Award: 2012
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Abstract:
This project describes a novel approach to the development of a multimodal video segmentation system for the analysis of multimedia data. The current practices of multimedia data analysis rely either solely on one of the video and audio components or on the presence of both together. The proposed approach makes use of both the video and audio inputs in parallel, complementing each other during the video processing stage, towards optimising both the accuracy and speed of the method. Unlike in the other commonly established methods, the video analysis here is carried out using both the luminance and the chrominance values of the colour images, instead of relying on either of them. The approach considered in the proposed method of video cut detection primarily uses a modified luminance based histogram analysis algorithm, supported by the additional sub-sampling and median filtering options. They improve the efficiency of the method through enhancing its speed and the accuracy of detection respectively. The algorithm mentioned above uses a progressively varying threshold for indicating a significant variation in the measurement of successive histograms for a window length of 2 image frames. The method worked successfully for the videos with varying rates and sizes of the frames that have been under investigation. Because of the degrading effect of chrominance histogram analysis on the processing speed its use is kept to a minimum. This is restricted only to verify the existence of possible cuts, failed to be identified by the luminance analysis. The indication of such cuts could be obtained through audio classification analysis.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.631732  DOI: Not available
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