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首页> 外文会议>2016 International Conference for Students on Applied Engineering >Biologically-inspired object recognition system for recognizing natural scene categories
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Biologically-inspired object recognition system for recognizing natural scene categories

机译：受生物启发的物体识别系统，用于识别自然场景类别

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Visual processing has attracted a lot of attention in the last decade. Hierarchical approaches for object recognition are gradually becoming widely-accepted. Generally, they are inspired by the ventral stream of human visual cortex, which is in charge of rapid categorization. Similar to objects, natural scenes share common features and can, therefore, be classified in the same manner. However, natural scenes generally show a high level of statistical correlation between classes. This, in fact, is a major challenge for most object recognition models. Rapid categorization of a natural scene in the absence of attention is a challenge. However, researchers have found that 150 ms is enough to categorize a complex natural scene. We tested the capability of our recent and bio-inspired En-HMAX model of visual processing for scene classification. The results show the En-HMAX model has a comparable performance to state of the art methods for natural scene categorization.
机译：在过去的十年中，视觉处理引起了很多关注。用于对象识别的分层方法逐渐被广泛接受。通常，它们受到负责快速分类的人类视觉皮层腹侧流的启发。与物体相似，自然场景具有共同的特征，因此可以以相同的方式进行分类。但是，自然场景通常在类之间显示出很高的统计相关性。实际上，这是大多数对象识别模型的主要挑战。在没有关注的情况下对自然场景进行快速分类是一个挑战。但是，研究人员发现150 ms足以对复杂的自然场景进行分类。我们测试了我们最新的，受生物启发的En-HMAX视觉处理模型对场景分类的功能。结果表明，En-HMAX模型的性能与自然场景分类的最新方法相当。

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