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A unified image retrieval framework on local visual and semantic concept-based feature spaces

机译:基于局部视觉和语义概念的特征空间的统一图像检索框架

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摘要

This paper presents a learning-based unified image retrieval framework to represent images in local visual and semantic concept-based feature spaces. In this framework, a visual concept vocabulary (code-book) is automatically constructed by utilizing self-organizing map (SOM) and statistical models are built for local semantic concepts using probabilistic multi-class support vector machine (SVM). Based on these constructions, the images are represented in correlation and spatial relationship-enhanced concept feature spaces by exploiting the topology preserving local neighborhood structure of the codebook, local concept correlation statistics, and spatial relationships in individual encoded images. Finally, the features are unified by a dynamically weighted linear combination of similarity matching scheme based on the relevance feedback information. The feature weights are calculated by considering both the precision and the rank order information of the top retrieved relevant images of each representation, which adapts itself to individual searches to produce effective results. The experimental results on a photographic database of natural scenes and a bio-medical database of different imaging modalities and body parts demonstrate the effectiveness of the proposed framework.
机译:本文提出了一种基于学习的统一图像检索框架,用于在基于本地视觉和语义概念的特征空间中表示图像。在此框架中,利用自组织映射(SOM)自动构建视觉概念词汇表(代码本),并使用概率多类支持向量机(SVM)为本地语义概念构建统计模型。基于这些构造,通过利用保留编码本的局部邻域结构,局部概念相关性统计信息和单个编码图像中的空间关系的拓扑,在相关性和空间关系增强的概念特征空间中表示图像。最后,通过基于相关性反馈信息的相似度匹配方案的动态加权线性组合来统一特征。特征权重是通过同时考虑每个表示的顶部检索相关图像的精度和等级顺序信息来计算的,从而使自身适合于单个搜索以产生有效的结果。在自然场景的照片数据库和不同成像方式和身体部位的生物医学数据库上的实验结果证明了所提出框架的有效性。

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