School of Electronics Engineering, Kyungpook National University, 1370 Sankyuk-Dong, Puk-Gu, Taegu 702-701, South Korea.
Neural Netw. 2012 Jan;25(1):130-40. doi: 10.1016/j.neunet.2011.06.020. Epub 2011 Jul 7.
This paper presents an adaptive object recognition model based on incremental feature representation and a hierarchical feature classifier that offers plasticity to accommodate additional input data and reduces the problem of forgetting previously learned information. The incremental feature representation method applies adaptive prototype generation with a cortex-like mechanism to conventional feature representation to enable an incremental reflection of various object characteristics, such as feature dimensions in the learning process. A feature classifier based on using a hierarchical generative model recognizes various objects with variant feature dimensions during the learning process. Experimental results show that the adaptive object recognition model successfully recognizes single and multiple-object classes with enhanced stability and flexibility.
本文提出了一种基于增量特征表示和层次特征分类器的自适应目标识别模型，该模型具有可塑性，可以适应额外的输入数据，同时减少了忘记先前学习信息的问题。增量特征表示方法采用皮质样机制的自适应原型生成，将其应用于常规特征表示，从而能够在学习过程中增量地反映各种对象特征，例如特征维度。基于使用分层生成模型的特征分类器可以在学习过程中识别具有不同特征维度的各种对象。实验结果表明，自适应目标识别模型成功地识别了单一和多类目标，具有增强的稳定性和灵活性。
