A content-based WMS layer retrieval method combining multiple kernel learning and user feedback

doi:10.11947/j.AGCS.2019.20180410

Abstract

Abstract: To facilitate the discovery and use of geographic information, it is necessary to design an effective retrieval strategy to locate the map layers that customers want from massive WMS resources. Existing text-based WMS retrieval strategies are unable to solve the problems of metadata loss and inconsistency between pictures and metadata text, without considering map content. The visual similarity between maps is used to design a WMS layer retrieval method that combines multi-feature multiple kernel learning and user feedback to help users search for desired WMS layers. Color, shape and texture features are fused by multiple kernel learning to classify and rank layers according to similarity. A feedback mechanism is also established in the retrieval strategy, which is an effective guarantee that improves accuracy by collecting user-marked layers. Various kinds of WMS layers are selected to calculate the precision ration, analyze the time cost, and validate the retrieval feedback mechanism. The experimental results of selected WMS layers verified that the proposed algorithm is fast and highly precise. It can be integrated with existing text-based retrieval and discovery portals of geographic information.

Key words: geographic information resource retrieval, multiple kernel learning, multi-feature fusion, user feedback, web map service

CLC Number:

P208

LI Muxian, GUI Zhipeng, CHENG Xiaoqiang, WU Huayi, QIN Kun. A content-based WMS layer retrieval method combining multiple kernel learning and user feedback[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(10): 1320-1330.

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