Iterative bilinear similarity measure learning for CBIR via a minimization of a local sensitivty

Shao-Hua Yin, Jincheng Li
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Abstract

With the fast development of photographic device and the Internet, millions of images have been uploaded to the Internet. So, there is an increasing needs of image retrieval method for large scale databases. Content Based Image retrieval (CBIR) is very useful when user wants to find some images that are similar to a given image in both visual and content. In this paper, we first summarize the research development of this field. Secondly, OASIS [5] (Online Algorithm for Scalable Image Similarity) is introduced. However, OASIS focuses on the difference of similarity values between relevant and irrelevant image pairs while ignores the similarity value between highly similar images. So we proposed an improvement to OASIS via a minimization of local Q-neighborhood's sensitivity. It provides a better generalization and retrieves more near duplicate and highly similar images. The proposed improvement is compared with the original OASIS method and yields a better performance.
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基于局部灵敏度最小化的迭代双线性相似性度量学习
随着摄影设备和互联网的快速发展,数以百万计的图像被上传到互联网上。因此,对大规模数据库的图像检索方法的需求越来越大。当用户希望找到与给定图像在视觉和内容上都相似的图像时,基于内容的图像检索(CBIR)非常有用。本文首先对该领域的研究进展进行了综述。其次,介绍了OASIS [5] (Online Algorithm for Scalable Image Similarity)。然而,OASIS关注的是相关和不相关图像对之间相似度值的差异,而忽略了高度相似图像之间的相似度值。因此我们提出了一种通过最小化局部q邻域灵敏度来改进OASIS的方法。它提供了更好的泛化和检索更多的近重复和高度相似的图像。将改进后的方法与原有的OASIS方法进行了比较,得到了更好的性能。
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