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. Different tasks of prototype search type can be solved in this alternative way depending on suitable criterion definition. The usability of the concept is shown on keyword extraction example. Further possible applications are suggested
Keyword spotting becomes a very important branch of speech recognition. But the acoustic mismatch between training and testing environments often causes a severe degradation in the recognition performance. This paper presents an improved keyword spotting strategy. A fuzzy search algorithm is proposed to extract
We have become able to get enough approvable images of a target object just by submitting its object-name to a conventional keyword-based Web image search engine. However, because the search results rarely include its uncommon images, we can often get only its common images and cannot easily get exhaustive knowledge
system to assist the users in easily accessing the information and have an enjoyable experience browsing Kotenseki images. There are two main functions comprising keyword-based and image-based queries. We also provide automatic detection of objects within the original images to create a database of feature vectors. Our
, our learnt model via listwise supervision is shown to be powerful for keyword-based image search with superior performance over several state-of-the-art methods.
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