Deriving Perceptually-Based Texture and Color Features for Image Segmentation, Categorization, and Retrieval

大量数字图像的快速积累产生了对用于图像检索的高效和智能方案的需求。 由于人类是大多数检索系统的最终用户，因此根据有意义的类别对内容进行语义组织非常重要。 这需要理解人类用于图像分类的重要语义类别，以及提取可以区分这些类别的有意义的图像特征。 最近的研究工作已经解决了第一个问题，但第二个问题仍然很难解决。 本研究的目的是解决第二个问题，即提取低层次的图像特征，这些特征可以与高层语义相关，并用于捕获图像的语义含义，本研究的关键是发展一种新的方法来分割图像，基于感知模型和纹理和颜色信息的处理原则。 这涉及到识别语义上重要的，空间自适应的，低层次的颜色和纹理特征，可以结合算法，以获得传达语义信息的图像分割。 可以使用相同的感知模型和原理来关联分割区域的特征（颜色和纹理特征，以及段的位置，大小和边界形状）的语义概念，可用于基于内容的图像检索。本研究的一个组成部分是主观实验的设计和执行，以获得一些关键参数的颜色和纹理特征，以及用于将低级图像特征链接到图像语义。

The rapid accumulation of large collections of digital images has created the need for efficient and intelligent schemes for image retrieval. Since humans are the ultimate users of most retrieval systems, it is important to organize the contents semantically, according to meaningful categories. This requires an understandingof the important semantic categories that humans use for image classification, and the extraction of meaningful image features that can discriminate between these categories. Recent research efforts have addressed the first problem, but the second remains quite elusive. This research effort is aimed at addressing this second problem, that is, the extraction of low-level image features that can be correlated with high-level semantics and used to capture the semantic meaning of an image.The key to this research is the development of a new methodology for segmenting images, based on perceptual models and principles about the processing of texture and color information. This involves the identification of semantically important, spatially adaptive, low-level color and texture features that can be combined algorithmically to obtain image segmentations that convey semantic information. The same perceptual models and principles can be used to relate the features of the segmented regions (color and texture features, as well as segment location, size, and boundary shape) to semantic concepts that can be used for content-based image retrieval.An integral part of this research is the design and execution of subjective experiments in order to obtain some key parameters for the color and texture features, as well as for linking low-level image features to image semantics.