In the standard bag-of-visual-words model, the relationship between visual words and geometric structure information embedding in Voronoi cells is important for expressing the topology of the feature space. However, this information is usually ignored by recent works. To overcome it, the authors proposed a hybrid heterogeneous structure model (HHSM), where local hyperspheres and local structure subspaces are applied to simulate the intrinsic structure of the feature space. Firstly, the local hypersphere is formed by choosing some links between parts of visual words, with the use of a proposed decision strategy derived from k -dense neighbour algorithm. In order to capture the geometric structure information around the visual word, they then construct the local structure subspace with the transformed PCA principal vectors of the visual features within a Voronoi cell. Finally, this study introduces a novel feature encoding method based on the HHSM. Experiments are conducted on 15-Scenes, Pascal VOC2007, Caltech101, Caltech256 and MIT Indoor 67 datasets, which include 4485, 9963, 9146, 30607 and 15620 images, respectively. The results demonstrate the effectiveness of the proposed method in improving the accuracy of the classification. In addition, the proposed method achieves comparable performance when combined with CNN local features.

中文翻译：

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基于混合异构结构模型的特征编码用于图像分类

在标准的视觉词袋模型中，视觉词与Voronoi单元中嵌入的几何结构信息之间的关系对于表达特征空间的拓扑结构很重要。但是，最近的工作通常忽略了这些信息。为了克服它，作者提出了一种混合异质结构模型（HHSM），其中将局部超球面和局部结构子空间应用于模拟特征空间的固有结构。首先，通过使用从视觉派生的拟议决策策略，选择视觉单词各部分之间的某些链接，形成局部超球体ķ 密集邻居算法。为了捕获视觉词周围的几何结构信息，他们随后使用Voronoi单元内视觉特征的转换后PCA主向量构造局部结构子空间。最后，本研究介绍了一种基于HHSM的新颖特征编码方法。实验在15个场景，Pascal VOC2007，Caltech101，Caltech256和MIT Indoor 67数据集上进行，分别包含4485，9963，9146，30607和15620个图像。结果证明了该方法在提高分类准确率方面的有效性。此外，当与CNN局部特征结合使用时，所提出的方法可获得相当的性能。