Hardoon David R, Szedmak Sandor, Shawe-Taylor John
School of Electronics and Computer Science, Image, Speech and Intelligent Systems Research Group, University of Southampton, Southampton S017 1BJ, UK.
Neural Comput. 2004 Dec;16(12):2639-64. doi: 10.1162/0899766042321814.
We present a general method using kernel canonical correlation analysis to learn a semantic representation to web images and their associated text. The semantic space provides a common representation and enables a comparison between the text and images. In the experiments, we look at two approaches of retrieving images based on only their content from a text query. We compare orthogonalization approaches against a standard cross-representation retrieval technique known as the generalized vector space model.
我们提出了一种使用核典型相关分析的通用方法，以学习网络图像及其相关文本的语义表示。语义空间提供了一种通用表示，并能够对文本和图像进行比较。在实验中，我们研究了两种仅基于文本查询从图像内容中检索图像的方法。我们将正交化方法与一种称为广义向量空间模型的标准交叉表示检索技术进行比较。
