针对场景分类任务中全局Gist特征粒度较为粗糙的问题,提出一种基于稠密网格的局部Gist特征描述,利用空间金字塔结构加入空间信息,通过引入RGB颜色空间加入颜色信息,并基于词汇包(BOW)模型设计一种高效匹配核来度量局部特征间的相似性,核化特征匹配过程,使用线性SVM完成场景分类。实验考察了不同尺度、方向、粒度和不同匹配核的局部Gist特征以及训练样本集的大小对分类结果的影响,并通过在OT场景图像集上与全局Gist特征和稠密SIFT特征的场景分类结果进行比较,充分说明了本文特征构造方法和分类模型的有效性。

Due to the coarse fineness of global Gist features in scene categorization tasks, we propose a local Gist feature description based on a dense grid. It uses a spatial pyramid structure to add distribution information and introduces the RGB color space to add color information. The feature matching process is kernelized by an efficient match kernel which mea-sures the similarity between local features based on the BOW model. The scene categorization task can be done with linear SVM. Experiment shows the influence to the classification accuracy with local Gist features which have different scale, orientation, fineness, match kernels and numbers of training samples. By using the classification result of the global Gist feature and dense SIFT features on the OT scene dataset, we demonstrate that the proposed feature construction method and classification model are efficient.