Learning and Representing Object Shape Through an Array of Orientation Columns

Recognizing an object from its background is always a very challenging task for pattern recognition, especially when the size, pose, or illumination of the object or the background are changing. The most essential method of handling this classical problem is to learn and define the structure of an object using its topological or geometrical features and components. To create a data structure that can describe the spatial relationships of object components formally and join knowledge learning and applying in a seamless loop, a representation platform must be developed. This platform can serve as a shared workspace not only for learning but also for recognition. In this paper, the platform is established by simulating the primary functional modules in the biological primary visual cortex (V1). V1 is located at the middle level of the visual information processing system. As the conjunction of low-level data and high-level knowledge, it performs visual processing for general purposes. Orientation columns in V1 are simulated in our platform, and an array of such columns is designed to represent the orientation features of edges in an image. With this platform, formalized prototypes are designed to represent each typical view of an object and thus the object concept. Data- and concept-driven processing can shift iteratively on this platform. The processes of acquiring knowledge of an object and applying that knowledge coincide with each other perfectly. The experimental results show that our algorithm can learn from a small training set and can recognize the same types of objects in natural background without any preliminary information. This bioinspired representation platform offers a promising prospect for the handling of semantic-concerned problems that need prior knowledge.