An elastic net-regularized HMAX model of visual processing
An elastic net-regularized HMAX model of visual processing
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DOI：
10.1049/cp.2015.1753
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Ali Alameer;G. Ghazaeil;P. Degenaar;K. Nazarpour
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文献类型：
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作者：
Ali Alameer;G. Ghazaeil;P. Degenaar;K. Nazarpour
人类视觉系统的分层最大（HMAX）模型已广泛应用于机器人和自主系统中。然而，在观察环境和对物体进行智能分类方面，人类和机器人视觉之间仍然存在明显差距。因此，改进HMAX等模型仍然是热门话题。在这项工作中，为了增强 HMAX 在对象识别任务中的性能，我们使用弹性网络正则化字典学习方法对其进行了增强。我们在 HMAX 模型的 S 层中使用稀疏编码的概念来从输入图像中提取中高级（即抽象）特征。此外，我们在较高层的输出处使用空间金字塔池化（SPP）来创建固定的特征向量，然后将其输入到 softmax 分类器中。在我们的模型中，使用弹性网络正则化字典学习算法计算的稀疏系数来训练和测试模型。通过此设置，我们实现了 5 倍平均分类精度为 82.6387%∓3.7183%，这明显优于原始 HMAX 所实现的分类精度。
The hierarchical MAX (HMAX) model of human visual system has been used in robotics and autonomous systems widely. However, there is still a stark gap between human and robotic vision in observing the environment and intelligently categorizing the objects. Therefore, improving models such as the HMAX is still topical. In this work, in order to enhance the performance of HMAX in an object recognition task, we augmented it using an elastic net-regularised dictionary learning approach. We used the notion of sparse coding in the S layers of the HMAX model to extract mid- and high-level, i.e. abstract, features from input images. In addition, we used spatial pyramid pooling (SPP) at the output of higher layers to create a fixed feature vectors before feeding them into a softmax classifier. In our model, the sparse coefficients calculated by the elastic net-regularised dictionary learning algorithm were used to train and test the model. With this setup, we achieved a classification accuracy of 82.6387%∓3.7183% averaged across 5-folds which is significantly better than that achieved with the original HMAX.