Wang R
Engineering Department, Harvey Mudd College, Claremont, CA 91711, USA.
Network. 2001 Nov;12(4):493-512.
A three-layer neural network is presented as a generic approach for visual pattern recognition invariant with respect to the geometric appearance such as translation, orientation and scale of the patterns. The invariant recognition is achieved by representing the geometric variations internally in the network by nodes in the input and middle layers, which are laterally connected and trained by a hybrid algorithm combining both competitive and Hebbian learning. As the result of the hybrid learning, each pattern will be represented by a particular subset of middle-layer nodes all specialized to respond to the same pattern but with different geometric appearances. The nodes in the output layer are then trained by competitive learning to recognize the different pattern internally represented by the middle-layer nodes, independent of their location, orientation and size. The proposed algorithm is generic and robust and can be applied to various practical recognition problems. Moreover, the network is relatively simple and biologically plausible and can serve as a computational model to account for the invariant object recognition in the biological visual system.
提出了一种三层神经网络，作为一种通用方法用于视觉模式识别，该方法对于诸如模式的平移、方向和比例等几何外观具有不变性。通过在网络内部由输入层和中间层的节点来表示几何变化，实现不变性识别，这些节点横向连接，并通过结合竞争学习和赫布学习的混合算法进行训练。作为混合学习的结果，每个模式将由中间层节点的特定子集表示，所有这些节点都专门用于响应相同的模式，但具有不同的几何外观。然后通过竞争学习对输出层中的节点进行训练，以识别由中间层节点内部表示的不同模式，而与它们的位置、方向和大小无关。所提出的算法具有通用性和鲁棒性，可应用于各种实际识别问题。此外，该网络相对简单且具有生物学合理性，可作为一种计算模型来解释生物视觉系统中的不变物体识别。
