An Image Feature Extraction Method Based on GaborSIFT+NNScSPM
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摘要: 视觉信息的特征表示是计算机视觉场景图像理解研究中的核心内容. 基于GaborSIFT+NNScSPM的图像特征抽取算法,借鉴生物视觉机制中的相关 研究成果,有机结合了HMAX层次计算模型的思想和非负稀疏编码的策略, 较为合理地模拟了生物视觉皮层中视觉处理的过程.在15类场景图 像和Caltech101两个公开数据集上进行了实验验证, 实验结果表明我们所提出的算法较同期算法有着良好的分类性能.Abstract: Feature representation of visual information is one of core research topics in computer vision and image understanding. In this paper, we propose a feature extraction method based on GaborSIFT+NNScSPM, trying to combine HMAX model with non-negative sparse coding to mimic the information process in V1 area in visual cortex. We have test our proposed method on two public data sets (15 scenes and Caltech101), and the experiment results show that our method outperforms the existing ones.
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