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Landmark Classification With Hierarchical Multi-Modal Exemplar Feature

机译：具有分级多模态特征的地标分类

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摘要

Landmark image classification attracts increasing research attention due to its great importance in real applications, ranging from travel guide recommendation to 3-D modelling and visualization of geolocation. While large amount of efforts have been invested, it still remains unsolved by academia and industry. One of the key reasons is the large intra-class variance rooted from the diverse visual appearance of landmark images. Distinguished from most existing methods based on scalable image search, we approach the problem from a new perspective and model landmark classification as multi-modal categorization , which enjoys advantages of low storage overhead and high classification efficiency. Toward this goal, a novel and effective feature representation, called hierarchical multi-modal exemplar (HMME) feature, is proposed to characterize landmark images. In order to compute HMME, training images are first partitioned into the regions with hierarchical grids to generate candidate images and regions. Then, at the stage of exemplar selection, hierarchical discriminative exemplars in multiple modalities are discovered automatically via iterative boosting and latent region label mining. Finally, HMME is generated via a region-based locality-constrained linear coding (RLLC), which effectively encodes semantics of the discovered exemplars into HMME. Meanwhile, dimension reduction is applied to reduce redundant information by projecting the raw HMME into lower-dimensional space. The final HMME enjoys advantages of discriminative and linearly separable. Experimental study has been carried out on real world landmark datasets, and the results demonstrate the superior performance of the proposed approach over several state-of-the-art techniques.
机译：具有里程碑意义的图像分类由于其在实际应用中的重要性而引起了越来越多的研究关注，从旅行指南推荐到3-D建模和地理位置可视化。尽管已经投入了大量的精力，但学术界和工业界仍未解决。关键原因之一是类内部差异很大，这源于地标图像的多样化外观。与大多数基于可伸缩图像搜索的现有方法不同，我们从一个新的角度解决了这一问题，并将地标分类称为多模式分类，它具有存储开销低和分类效率高的优点。为了实现这一目标，提出了一种新颖而有效的特征表示方法，称为分层多模式样例（HMME）特征，以表征地标图像。为了计算HMME，首先将训练图像划分为具有分层网格的区域，以生成候选图像和区域。然后，在示例选择阶段，通过迭代增强和潜在区域标签挖掘自动发现多种模态中的分层判别示例。最后，HMME是通过基于区域的局部约束线性编码（RLLC）生成的，该编码有效地将发现的示例的语义编码为HMME。同时，通过将原始HMME投影到低维空间中，应用了降维以减少冗余信息。最终的HMME具有判别性和线性可分离性的优点。在现实世界中的地标数据集上进行了实验研究，结果证明了该方法优于几种最新技术的性能。

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