IBM T. J.Watson Research Center, Hawthorne, NY 10532, USA.
IEEE Trans Image Process. 2002;11(11):1238-48. doi: 10.1109/TIP.2002.804260.
Color descriptors are among the most important features used in image analysis and retrieval. Due to its compact representation and low complexity, direct histogram comparison is a commonly used technique for measuring the color similarity. However, it has many serious drawbacks, including a high degree of dependency on color codebook design, sensitivity to quantization boundaries, and inefficiency in representing images with few dominant colors. In this paper, we present a new algorithm for color matching that models behavior of the human visual system in capturing color appearance of an image. We first develop a new method for color codebook design in the Lab space. The method is well suited for creating small fixed color codebooks; for image analysis, matching, and retrieval. Then we introduce a statistical technique to extract perceptually relevant colors. We also propose a new color distance measure that is based on the optimal mapping between two sets of color components representing two images. Experiments comparing the new algorithm to some existing techniques show that these novel elements lead to better match to human perception in judging image similarity in terms of color composition.
颜色描述符是图像分析和检索中最重要的特征之一。由于其紧凑的表示形式和低复杂度，直接直方图比较是一种常用的测量颜色相似性的技术。然而，它有许多严重的缺点，包括对颜色码本设计的高度依赖性、对量化边界的敏感性以及在表示颜色较少的图像时效率低下。在本文中，我们提出了一种新的颜色匹配算法，该算法模拟了人眼视觉系统捕获图像颜色外观的行为。我们首先在 Lab 空间中开发了一种新的颜色码本设计方法。该方法非常适合创建小的固定颜色码本；用于图像分析、匹配和检索。然后，我们引入了一种用于提取感知相关颜色的统计技术。我们还提出了一种新的颜色距离度量，该度量基于代表两幅图像的两组颜色分量之间的最佳映射。将新算法与一些现有技术进行比较的实验表明，这些新元素在根据颜色组成判断图像相似性方面，与人眼感知的匹配更好。
