In this paper, we describe a novel local descriptor of image texture representation for visual recognition. The image features based on micro-descriptors such as local binary patterns (LBP) and local ternary patterns (LTP) have been very successful in a number of applications including face recognition, object detection, and texture analysis. Instead of binary quantization in LBP, LTP thresholds the differential values between a focused pixel and its neighborhood pixels into three gray levels, which can be explained as the active status (i.e., positively activated, negatively activated, and not activated) of the neighborhood pixels compared to the focused pixel. However, regardless of the magnitude of the focused pixel, the thresholding strategy remains fixed, which would violate the principle of human perception. Therefore, in this study, we design LTP with a data-driven threshold according to Weber’s law, a human perception principle; further, our approach incorporates the contexts of spatial and orientation co-occurrences (i.e., co-occurrence context) among adjacent Weber-based local ternary patterns (WLTPs, i.e., data-driven quantized LTPs) for texture representation. The explored WLTP is formulated by adaptively quantizing differential values between neighborhood pixels and the focused pixel as negative or positive stimuli if the normalized differential values are large; otherwise, the stimulus is set to 0. Our approach here is based on the fact that human perception of a distinguished pattern depends not only on the absolute intensity of the stimulus but also on the relative variance of the stimulus. By integrating co-occurrence context information, we further propose a rotation invariant co-occurrence WLTP (RICWLTP) approach to be more discriminant for image representation. In order to validate the efficiency of our proposed strategy, we apply this to three different visual recognition applications including two texture datasets and one food image dataset and prove the promising performance that can be achieved compared with the state-of-the-art approaches.

中文翻译：

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用于视觉识别的数据驱动量化局部三元模式的共现上下文

在本文中，我们描述了一种用于视觉识别的图像纹理表示的新型局部描述符。基于微描述符的图像特征（例如本地二进制模式（LBP）和本地三进制模式（LTP））在包括面部识别，对象检测和纹理分析在内的许多应用中都非常成功。代替LBP中的二进制量化，LTP将聚焦像素及其邻域像素之间的差分值阈值化为三个灰度级，这可以解释为邻域像素的活动状态（即，正激活，负激活和未激活）与聚焦像素相比 但是，无论聚焦像素的大小如何，阈值策略都保持固定，这将违反人类感知的原理。因此，在这项研究中 我们根据韦伯定律和人类感知原理设计具有数据驱动阈值的LTP；此外，我们的方法在纹理表示的相邻基于Weber的本地三元模式（WLTP，即数据驱动的量化LTP）之间结合了空间和方向共生的上下文（即，共现上下文）。如果归一化的微分值较大，则通过自适应地将邻域像素和聚焦像素之间的微分值量化为负或正刺激来制定探索的WLTP。否则，将刺激设置为0。此处的方法基于这样一个事实，即人类对特定模式的感知不仅取决于刺激的绝对强度，还取决于刺激的相对方差。通过整合同现上下文信息，我们还提出了旋转不变共现WLTP（RICWLTP）方法，以便对图像表示进行更多区分。为了验证我们提出的策略的效率，我们将此方法应用于三种不同的视觉识别应用程序，其中包括两个纹理数据集和一个食物图像数据集，并证明了与最新技术相比可以实现的有希望的性能。