Abstract

Object recognition occurs accurately with human visual neural mechanism despite in different complex background interference. For computer system, it is still a challenging work of object recognition and classification. Recently, many methods for object recognition based on human visual perception mechanism are presented. However, most methods cannot achieve a better recognition accuracy when object images are corrupted by some background interferences. Therefore, it is necessary to propose a method for object recognition of complex scene. Inspired by biomimetic visual mechanism and visual memory, a multi-channel biomimetic visual transformation (MCBVT) is proposed in this paper. MCBVT involves three channels. Firstly, some algorithms including orientation edge detection (OED), local spatial frequency detection (LSFD) and weighted centroid coordinate calculation are adopted for two stage’s visual memory maps creations during the first channel, where some visual memory points are stored in memory map. Secondly, an object hitting map (OHM) is built in the second channel and the OHM is an edge image without background interference. After that, the first stage’s visual memory hitting map is obtained through execute back-tracking second stage’s visual memory map. Furthermore, an OHM is constructed through back-tracking with common memory points in first stage’s visual memory map and first stage’s visual memory hitting map. Thirdly, the OED and LSFD algorithms are conducted to extract a feature map of OHM in the third channel. Consequently, the final feature map is reshaped into a feature vector, which is used for object recognition. Additionally, several image database experiments are implemented, the recognition accuracy for alphanumeric, MPEG-7 and GTSRB database are 93.33%, 91.33 and 90% respectively. Moreover, same object images in different backgrounds share with highly similar feature maps. On the contrary, different object images with complex backgrounds through MCBVT show different feature maps. The experiments reveal a better selectivity and invariance of MCBVT features. In summary, the proposed MCBVT provides a new framework of feature extraction. Background interference of object image is eliminated through the first and second channel, which is a new method for background noise reduction. Meanwhile, the results show that the proposed MCBVT method is better than other feature extraction methods. The contributions of this paper is significant in computational intelligence for the further work.

中文翻译：

---

多通道仿生视觉转换，用于物体特征提取和复杂场景识别

摘要

尽管存在不同的复杂背景干扰，但通过人类视觉神经机制可以准确地进行目标识别。对于计算机系统而言，它仍然是物体识别和分类的一项艰巨的工作。近年来，提出了许多基于人类视觉感知机制的物体识别方法。但是，当物体图像由于某些背景干扰而损坏时，大多数方法都无法获得更好的识别精度。因此，有必要提出一种用于复杂场景的物体识别的方法。在仿生视觉机制和视觉记忆的启发下，提出了一种多通道仿生视觉转换（MCBVT）。MCBVT涉及三个通道。首先，一些算法包括定向边缘检测（OED），在第一通道期间，两阶段的视觉记忆图创建采用局部空间频率检测（LSFD）和加权质心坐标计算，其中一些视觉记忆点存储在记忆图中。其次，在第二通道中建立了一个物体撞击图（OHM），OHM是没有背景干扰的边缘图像。之后，通过执行回溯第二阶段的视觉记忆图来获得第一阶段的视觉记忆图。此外，通过对第一阶段的视觉记忆图和第一阶段的视觉记忆命中图中的公共记忆点进行回溯来构造OHM。第三，进行OED和LSFD算法提取第三通道的OHM特征图。因此，将最终的特征图重塑为特征向量，用于对象识别。此外，还进行了一些图像数据库实验，字母数字，MPEG-7和GTSRB数据库的识别精度分别为93.33％，91.33和90％。此外，不同背景下的相同对象图像与高度相似的特征图共享。相反，通过MCBVT具有复杂背景的不同对象图像显示了不同的特征图。实验表明，MCBVT功能具有更好的选择性和不变性。总之，提出的MCBVT提供了一种新的特征提取框架。通过第一通道和第二通道消除了物体图像的背景干扰，这是一种降低背景噪声的新方法。同时，结果表明，所提出的MCBVT方法优于其他特征提取方法。