Approximations in the HMAX Model
Approximations in the HMAX Model
复制标题
HMAX 模型中的近似值
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
T. Poggio
中科院分区：
文献类型：
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作者：
S. Chikkerur;T. Poggio
HMAX模型是一种基于生物学动机的计算机视觉架构，其组成部分与现有的生理证据非常一致。该模型能够在几个快速目标识别任务上实现接近人类水平的性能。然而，该模型在计算上受到限制，并且以其当前形式具有有限的工程应用。在本报告中，我们提出了几个近似，以提高HMAX模型的效率。我们概述了近似的层次结构的几个层次和经验评估效率和准确性之间的权衡。我们还探讨了量化模型表示能力的方法。
The HMAX model is a biologically motivated architecture for computer vision whose components are in close agreement with existing physiological evidence. The model is capable of achieving close to human level performance on several rapid object recognition tasks. However, the model is computationally bound and has limited engineering applications in its current form. In this report, we present several approximations in order to increase the efficiency of the HMAX model. We outline approximations at several levels of the hierarchy and empirically evaluate the trade-offs between efficiency and accuracy. We also explore ways to quantify the representation capacity of the model.