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摘要: 提出了一种基于视觉知识加工模型的目标识别方法. 该加工模型结合目标定位、模板筛选和MFF-HMAX (Hierarchical model and X based on multi-feature fusion)方法对图像进行学习, 形成相应的视觉知识库, 并用于指导目标的识别. 首先, 利用Itti模型获取图像的显著区, 结合视觉通路中What和Where通道的位置、大小等特征以及视觉知识库中的定位知识确定初期候选目标区域; 然后, 采用二步去噪处理获取候选目标区域, 利用MFF-HMAX模型提取目标区域的颜色、亮度、纹理、轮廓、大小等知识特征, 并采用特征融合思想将各项特征融合供目标识别; 最后, 与单一特征以及目前的流行方法进行对比实验, 结果表明本文方法不仅具备较高的识别效果, 同时能够模仿人脑学习视觉知识的过程形成视觉知识库.Abstract: A novel object recognition method based on visual knowledge processing model is presented. Combined with object localization, template screening and hierarchical model and X based on multi-feature fusion (MFF-HMAX) method, the visual knowledge processing model yields a visual knowledge base which can be used as a guide in object recognition. Firstly, significant areas of the image can be obtained via Itti model; according to these areas and "what" and "where" information, such as location, size, etc., the candidate objects are conformed. Secondly, MFF-HMAX model is used to extract various features, like color, intensity, texture, contour, size, etc., from the objects denoised by the two-step denoising process. After multi-feature fusion, the features can be used in object recognition. Finally, the method is tested and compared with single feature method and current popular methods. The results show that this method can not only get good performance in improving accuracy of object detection, but also yield a base of visual knowledge by imitating the forming process in human brain.
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图 2 基于视觉知识加工模型的目标识别方法图
Fig. 2 Object recognition method based on visual knowledge processing
图 4 原图与候选目标对象图的对比图
Fig. 4 Comparison between the original images and the candidate object
图 5 二步去噪处理后的轮廓信息图
Fig. 5 The contour information maps after two-step denoising processing
图 7 不同方法针对Caltech 101数据集的分类效果对比图
Fig. 7 Performance of different methods for Caltech 101
图 8 Caltech 101数据集不同类型的分类效果对比图
Fig. 8 Performance for different categories of Caltech 101
图 9 Pascal 2007数据集不同类型的分类效果对比图
Fig. 9 Performance for different categories of Pascal 2007
表 1 本文方法参数设置
Table 1 Parameters setting of our method
Band $\Sigma$ Filt sizes $\delta$ $\lambda$ $N$$^\Sigma$ Orient $\theta$ Patch $n_j$ 1 7 & 9 2.8 & 3.6 3.5 & 4.6 8 0 4$\times$4 2 11 & 13 4.5 & 5.4 5.6 & 6.8 10 3 15 & 17 6.3 & 7.3 7.9 & 9.1 12 $\dfrac{\pi}{4}$ 8$\times$8 4 19 & 21 8.2 & 9.2 10.3 & 11.5 14 5 23 & 25 10.2 & 11.3 12.7 & 14.1 16 $\dfrac{\pi}{2}$ 12$\times$12 6 27 & 29 12.3 & 13.4 15.4 & 16.8 18 7 31 & 33 14.6 & 15.8 18.2 & 19.7 20 $\dfrac{3\pi}{4}$ 14$\times$14 8 35 & 37 17.0 & 18.2 21.2 & 22.8 22 
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