30 October 2009 Two-stage high resolution remote sensing image retrieval combining semantic and visual features
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Proceedings Volume 7495, MIPPR 2009: Automatic Target Recognition and Image Analysis; 749550 (2009) https://doi.org/10.1117/12.832727
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China
Abstract
In this work, we put forward a two-stage image retrieval methodology by integrating high level image semantic features and low level visual features. At the first stage, we segment an image into parcels using a multiresolution remotely sensed image segmentation method combining rainfalling watershed algorithm and fast region merging. We then classify these parcels with Support Vector Machine (SVM), a famous non-linear classification scheme to connect the low-level visual features with high-level semantic features. These classes are then stored in semantic features databases for future use. When users carry out their rough semantic retrieval, they should choose and combine these semantic classes, and our method returns some image blocks which include the interested classes as the first "rough" retrieval results. At the second stage users should select an example from the results. We then construct and compare the similarity between the color and texture histograms for both the query example and each one in the semantic retrieval result. If the total similarity is higher than some threshold, the image will be returned as a suitable retrieval result. These images are sorted according their similarity as the final retrieval results. Experiments indicate our approach can get more effective and accurate results than content-based image retrieval only using visual features.
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Qi-Ming Wan, Qi-Ming Wan, Min Wang, Min Wang, Xing-Yue Zhang, Xing-Yue Zhang, Da-Qian Zhang, Da-Qian Zhang, } "Two-stage high resolution remote sensing image retrieval combining semantic and visual features", Proc. SPIE 7495, MIPPR 2009: Automatic Target Recognition and Image Analysis, 749550 (30 October 2009); doi: 10.1117/12.832727; https://doi.org/10.1117/12.832727
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