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vector of the wood image. This keyblock distribution based wood image retrieval algorithm is similar to the keyword based text retrieval algorithm. In the text retrieval algorithm, keyword can be used to represent the content of the text. Similarly, the keyblock in our proposed algorithm can be used to represent the content
retrieved from the CBIR system. Metadata of the result images which are similar to the query image are extracted from the metadata database. From the resulting metadata, common keywords are extracted and proposed as the keywords for the query image.
the age of Big Data where Velocity, Variety and Volume are the challenges, variety of data that include structured as well as unstructured data is the most important issue. Image mining in Big Data is the challenge need to be addressed, so the proposed work compose an image query object, aspect of use, Keywords to
region of initial retrieved results. Both keywords and image contents of the Web images are computed by LLSI to re-rank the initial retrieval results automatically. The PRF-LLSI contribute to the following: (1) Local LSI resolves the heavy computation cost of LSI; (2) Pseudo Relevance Feedback doesn't need the user's
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.