Rahmani Rouhollah, Goldman Sally A, Zhang Hui, Cholleti Sharath R, Fritts Jason E
One Microsoft Way, Redmond, WA 98052, USA.
IEEE Trans Pattern Anal Mach Intell. 2008 Nov;30(11):1902-12. doi: 10.1109/TPAMI.2008.112.
We define localized content-based image retrieval as a CBIR task where the user is only interested in a portion of the image, and the rest of the image is irrelevant. In this paper we present a localized CBIR system, Accio, that uses labeled images in conjunction with a multiple-instance learning algorithm to first identify the desired object and weight the features accordingly, and then to rank images in the database using a similarity measure that is based upon only the relevant portions of the image. A challenge for localized CBIR is how to represent the image to capture the content. We present and compare two novel image representations, which extend traditional segmentation-based and salient point-based techniques respectively, to capture content in a localized CBIR setting.
我们将基于内容的局部图像检索定义为一种CBIR任务，在该任务中用户仅对图像的一部分感兴趣，而图像的其余部分则无关紧要。在本文中，我们提出了一种局部CBIR系统Accio，该系统将带标签的图像与多实例学习算法结合使用，首先识别所需对象并相应地对特征进行加权，然后使用仅基于图像相关部分的相似性度量对数据库中的图像进行排名。局部CBIR面临的一个挑战是如何表示图像以捕获内容。我们提出并比较了两种新颖的图像表示方法，它们分别扩展了传统的基于分割和基于显著点的技术，以在局部CBIR设置中捕获内容。
