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This section describes how the excitement classification output is used in construction of the feature parameter for highlights extraction.
Next, we perform a subjective evaluation of the complete audio-video highlights extraction system combining the multi-modal features.
Along with this, audio energy, slow motion duration, camera motion, and scene-cut density were used as features in the highlights extraction system.
In section 4, the overall highlights extraction scheme is presented; section 5 details a subjective evaluation of the full system and discuses results, and section 6 concludes the study.
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Read&Write features visual highlighting of spoken text, the conversion of scanned documents into text documents, word prediction support, highlighting and highlight extraction, and the conversion of text documents into audio formats.
The sound processing is also applied quite often in video highlight extraction [15 19].
We propose to make use of the transition effects inserted by broadcasters for sports videos highlight extraction.
Cheng et al. [9] developed a baseball highlight extraction scheme based on HMM by fusing video and audio features.
In our opinions, the highlight extraction is an auxiliary function of a video recorder, which should not be computationally expensive.
In our initial study [15], we examined a simple audio/video feature fusion method for baseball highlight extraction.
Unless events are accurately delineated, HMM event classification will remain prone to errors and is not as versatile as fully automated highlight extraction demands in reality.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com