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- W2045083800 abstract "In this paper, a multi-level image representation model is developed and used for multi-spectral remote sensing imageretrieval in order to narrow the gap between the low-level feature and high-level semantic. This model consists of animage segmentation part, a feature extraction part and semantic extraction part. The first two parts aim at the extractionof primitive region feature of an image. In these two steps, an improved JSEG algorithm is used to segment the imagestored in the database, then spectral feature and texture feature are extracted for each region. In semantic extraction part,the semantic information hidden in different regions of different images is extracted by Bayesian method and expectationmaximization (EM) method. At last, positive example and negative example concept is used in image retrieval instead ofrelevant feedback. Experiment shows that this method not only improves the accuracy of the result but also decreases thecomplexity of retrieval." @default.
- W2045083800 created "2016-06-24" @default.
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- W2045083800 date "2008-12-28" @default.
- W2045083800 modified "2023-09-23" @default.
- W2045083800 title "Multi-spectral remote sensing image retrieval based on semantic extraction" @default.
- W2045083800 doi "https://doi.org/10.1117/12.815899" @default.
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