29 December 2008 Multi-spectral remote sensing image retrieval based on semantic extraction
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Proceedings Volume 7285, International Conference on Earth Observation Data Processing and Analysis (ICEODPA); 72850T (2008) https://doi.org/10.1117/12.815899
Event: International Conference on Earth Observation Data Processing and Analysis, 2008, Wuhan, China
Abstract
In this paper, a multi-level image representation model is developed and used for multi-spectral remote sensing image retrieval in order to narrow the gap between the low-level feature and high-level semantic. This model consists of an image segmentation part, a feature extraction part and semantic extraction part. The first two parts aim at the extraction of primitive region feature of an image. In these two steps, an improved JSEG algorithm is used to segment the image stored 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 expectation maximization (EM) method. At last, positive example and negative example concept is used in image retrieval instead of relevant feedback. Experiment shows that this method not only improves the accuracy of the result but also decreases the complexity of retrieval.
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Tingting Liu, Tingting Liu, Pingxiang Li, Pingxiang Li, Liangpei Zhang, Liangpei Zhang, Xu Chen, Xu Chen, } "Multi-spectral remote sensing image retrieval based on semantic extraction", Proc. SPIE 7285, International Conference on Earth Observation Data Processing and Analysis (ICEODPA), 72850T (29 December 2008); doi: 10.1117/12.815899; https://doi.org/10.1117/12.815899
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