Discriminative BoW Framework for Mobile Landmark Recognition
Discriminative BoW Framework for Mobile Landmark Recognition
复制标题
DOI：
10.1109/tcyb.2013.2267015
复制
发表时间：
2014-05-01
影响因子：
11.8
通讯作者：
Yap, Kim-Hui
中科院分区：
文献类型：
--
作者：
Chen, Tao;Yap, Kim-Hui
提出了一种新的基于图像块判别学习的移动地标识别软词袋方法。传统的弓方法通常认为图像中的斑块/区域对于学习同样重要。在已有的少数考虑斑块区分性信息的工作中，主要集中在选取具有代表性的斑块进行训练，而抛弃了其他的。这种二进制硬选择方法导致可用信息的未充分利用，因为一些被丢弃的补丁可能仍然包含有用的区别性信息。此外，并不是所有选定的补丁都会对学习过程做出同样的贡献。鉴于此，本文提出了一种新的用于移动地标识别的判别软弓方法。该方法的主要贡献是在三个层次上学习地标的代表性和区别性信息：斑块、图像和码字。首先学习每个地标的斑块区分信息，并通过矢量量化合并以生成柔弓直方图。结合学习到的图像和码字的代表性信息，这些直方图被用来使用模糊支持向量机来训练分类器集成。在两个不同的数据集上的实验结果表明，该方法在移动地标识别中是有效的。
This paper proposes a new soft bag-of-words (BoW) method for mobile landmark recognition based on discriminative learning of image patches. Conventional BoW methods often consider the patches/regions in the images as equally important for learning. Amongst the few existing works that consider the discriminative information of the patches, they mainly focus on selecting the representative patches for training, and discard the others. This binary hard selection approach results in underutilization of the information available, as some discarded patches may still contain useful discriminative information. Further, not all the selected patches will contribute equally to the learning process. In view of this, this paper presents a new discriminative soft BoW approach for mobile landmark recognition. The main contribution of the method is that the representative and discriminative information of the landmark is learned at three levels: patches, images, and codewords. The patch discriminative information for each landmark is first learned and incorporated through vector quantization to generate soft BoW histograms. Coupled with the learned representative information of the images and codewords, these histograms are used to train an ensemble of classifiers using fuzzy support vector machine. Experimental results on two different datasets show that the proposed method is effective in mobile landmark recognition.