Hsieh Jun-Wei, Grimson W Eric L
Dept. of Electr. Eng., Yuan Ze Univ., Taiwan, Taiwan.
IEEE Trans Image Process. 2003;12(11):1404-15. doi: 10.1109/TIP.2003.816013.
This paper presents a template and its relation extraction and estimation (TREE) algorithm for indexing images from picture libraries with more semantics-sensitive meanings. This algorithm can learn the commonality of visual concepts from multiple images to give a middle-level understanding about image contents. In this approach, each image is represented by a set of templates and their spatial relations as keys to capture the essence of this image. Each template is characterized by a set of dominant regions, which reflect different appearances of an object at different conditions and can be obtained by the template extraction and analysis (TEA) algorithm through region matching. The spatial template relation extraction and measurement (STREAM) algorithm is then proposed for obtaining the spatial relations between these templates. Due to the nature of a template, which can represent object's appearances at different conditions, the proposed approach owns better capabilities and flexibilities to capture image contents than traditional region-based methods. In addition, through maintaining the spatial layout of images, the semantic meanings of the query images can be extracted and lead to significant improvements in the accuracy of image retrieval. Since no time-consuming optimization process is involved, the proposed method learns the visual concepts extremely fast. Experimental results are provided to prove the superiority of the proposed method.
本文提出了一种模板及其关系提取与估计（TREE）算法，用于对具有更多语义敏感意义的图片库中的图像进行索引。该算法可以从多幅图像中学习视觉概念的共性，从而对图像内容有一个中级理解。在这种方法中，每幅图像由一组模板及其空间关系表示，作为捕捉该图像本质的关键。每个模板由一组主导区域表征，这些主导区域反映了物体在不同条件下的不同外观，并且可以通过模板提取与分析（TEA）算法通过区域匹配获得。然后提出了空间模板关系提取与测量（STREAM）算法，用于获取这些模板之间的空间关系。由于模板的性质，它可以表示物体在不同条件下的外观，因此所提出的方法比传统的基于区域的方法具有更好的能力和灵活性来捕捉图像内容。此外，通过保持图像的空间布局，可以提取查询图像的语义意义，并显著提高图像检索的准确性。由于不涉及耗时的优化过程，所提出的方法学习视觉概念非常快。提供了实验结果来证明所提出方法的优越性。
