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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