To address modeling problems of brain-inspired intelligence, this thesis is focused on researching in the semantic-oriented framework design for image, audio, language and video. The Multimedia Neural Cognitive Computing (MNCC) model was designed based on the nervous mechanism and cognitive architecture. Furthermore, the semantic-oriented hierarchical Cross-media Neural Cognitive Computing (CNCC) framework was proposed based on MNCC, and formal description and analysis for CNCC was given. It would effectively improve the performance of semantic processing for multimedia information, and has far-reaching significance for exploration and realization brain-inspired computing.
翻译:为解决大脑启发智能的建模问题,该论文的重点是研究图象、音频、语言和视频的语义框架设计;多媒体神经认知计算模型(MNCC)是根据神经机理和认知结构设计的;此外,根据MNCC提出了注重语义的跨媒体神经认知计算(CNCC)等级结构框架,为CNCC提供了正式描述和分析;这将有效地改善多媒体信息语义处理的性能,并对探索和实现大脑启发计算具有深远意义。