一种改进包模型的遥感图像检索方法

目的 提出了一种改进的包模型图像检索方法,使用本底图像构建视觉词汇字典。在建库过程中,通过近似KD-Tree搜索本底图像局部不变特征与入库图像特征间的对应匹配关系,并记录该关系;图像检索时,搜索检索图像与本底图像特征的对应关系,进而计算与入库图像的关系。该方法仅保存本底图像的局部不变特征,可实现感兴趣区域的图像检索。实验结果表明,该方法在提高图像检索准确性的同时,可以减少所需存储局部不变特征的数量。

An Improved Remote Sensing Image Retrieval MethodBased on Bag of Word Framework

Objective The Bag-of-Words(BoW)approach has lower retrieval accuracy performance for remotesensing image retrieval.The rate of correct results is very low especially when target textures are sim-ilar.To overcome this shortcoming,an indirect comparison between a query and target image basedon local invariant feature of the background image is proposed.The solution has two phases:theprocess of building a database and a retrieval process.After building a database,a search of the nea-rest neighbor feature in a query image in the feature space of the image dataset through an approximateKD-Tree is executed,then their relationship is recorded.The relationship can be used in calculatingsimilarity between a query and target image during a retrieval procedure.Experiments show that theproposed method has a better recognition performance than that of the BoW and,at the same time,needs less storage space for saving local invariant features.