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Content-based image retrieval using local visual attention feature

机译：使用本地视觉注意力功能的基于内容的图像检索

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摘要

Content-based image retrieval (CBIR) has been an active research topic in the last decade. As one of the promising approaches, salient point based image retrieval has attracted many researchers. However, the related work is usually very time consuming, and some salient points always may not represent the most interesting subset of points for image indexing. Based on fast and performant salient point detector, and the salient point expansion, a novel content-based image retrieval using local visual attention feature is proposed in this paper. Firstly, the salient image points are extracted by using the fast and performant SURF (Speeded-Up Robust Features) detector. Then, the visually significant image points around salient points can be obtained according to the salient point expansion. Finally, the local visual attention feature of visually significant image points, including the weighted color histogram and spatial distribution entropy, are extracted, and the similarity between color images is computed by using the local visual attention feature. Experimental results, including comparisons with the state-of-the-art retrieval systems, demonstrate the effectiveness of our proposal.
机译：基于内容的图像检索（CBIR）在过去十年中一直是活跃的研究主题。作为一种有前途的方法，基于显着点的图像检索吸引了许多研究人员。但是，相关工作通常非常耗时，并且某些显着点可能始终不能代表图像索引中最有趣的点子集。基于快速高效的显着点检测器和显着点扩展，提出了一种基于局部视觉注意力特征的基于内容的图像检索方法。首先，使用快速高效的SURF（快速鲁棒特征）检测器提取显着图像点。然后，可以根据显着点扩展获得显着点周围的视觉上有意义的图像点。最后，提取视觉重要图像点的局部视觉注意特征，包括加权的颜色直方图和空间分布熵，并利用局部视觉注意特征计算彩色图像之间的相似度。实验结果，包括与最新检索系统的比较，证明了我们建议的有效性。

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