Development of statistical object discrimination system using image characteristics

在这项研究中，我们开发了一个判别系统的图像对象的数据识别，这已经发展的统计研究，图像中的对象识别，并通过评估的概率，两个给定的图像是来自同一个对象。我们实现了（1）使用逻辑判别分析的对象识别系统的开发，以及（2）将分层人工神经网络引入到（1）中开发的系统中。并进行了以下相关研究：（3）纺织品图像特征与人感关系的研究;（4）统计判别的基础研究。下面，对（1）、（2）进行说明，省略（3）、（4）的说明。(1)我们开发了一个系统，该系统的区别与程度的信心，无论是在一个图像中的对象，并在另一个图像是在同一类别或在不同的类别。该系统测量受到若干图像特征影响的每一已知图像对之间的差异，并使用最大似然方法导出逻辑函数，该逻辑函数最佳地区分属于同一类别的图像对和属于不同类别的图像对。新的一对图像进行区分的置信度通过这个逻辑判别函数。我们将该系统应用于树叶图像的识别。(2)我们将（1）中开发的系统和3层神经网络统一起来。我们只将神经网络应用于错误率较高的情况，并开发了一个判别系统，该系统既具有清晰的逻辑斯蒂函数判别模型，又具有神经网络的高判别能力。新的混合系统实现了几乎相同的判别能力，作为原来的神经网络，并减少了计算时间的五分之一。

We developed in this research a discrimination system of image objects by applying the data identification, which had been already developed the statistical research, to object identification in images, and by evaluating the probability that two given images are derived from an identical object. We achieved that (1) development of an object identification system using the logistic discrimination analysis, and (2) introduction of the layered artificial neural network into the system developed in (1). We also carried out related researches as follows : (3) investigation of relationship between image characteristics and human kansei on textiles, and (4) fundamental research on statistical discrimination. We explain (1) and (2) in the following, and explanations of (3) and (4) are omitted here.(1) We developed a system that discriminates with the degree of confidence whether the object in an image and that in another image are in the same category or in the different categories. This system measures the differences between each known pair of images subject to several image characteristics, and derives using the maximum likelihood method the logistic function that optimally discriminates pairs belonging to the same category and those belonging to the different categories. A new pair of images is discriminated with the confidence degree by this logistic discrimination function. We applied this system to the discrimination of images of leaves.(2) We unified the system developed in (1) and the 3-layer neural network. We applied the neural network only for the cases that the error ratio is high, and developed a discrimination system that has the clear discrimination model of the logistic function as well as the high discrimination ability of the neural network. The new hybrid system achieved almost the same discrimination ability as the original neural network and reduced the computation time to one-fifth.