基于全局优化策略的场景分类算法

Scene Classification Based on Global Optimized Framework

提出一种基于全局优化策略的场景分类算法.该算法基于整幅图像提取全局场景特征——空间包络特征.从图像块中提取视觉单词,且定义隐变量表示该视觉单词语义,然后引入隐状态结构图描述整幅图像的视觉单词上下文;在场景分类策略上,构造由相容函数组成的目标函数,其中相容函数度量全局场景特征、隐变量与场景类别标记的相容度,通过求解目标函数的全局最优解推断图像的场景类别标记.在标准场景图像库上的对比实验表明该算法优于当前有代表性的场景分类算法.

A scene classification algorithm based on global optimized framework is proposed. Firstly, the global scene feature named spatial envelop is obtained from the whole image, the visual word of each image block is extracted, and latent variable is defined to represent the semantic feature of the extracted visual word. Secondly, the structure graph of latent state is introduced to represent the context of visual words. In respect to scene classification strategy, objective function consisting of different potential functions is constructed in which potential functions are defined to measure the relevance of the variables including global scene feature, latent variables and scene category. Finally, the scene category of the image is determined when the global optimized solution of objective function is obtained. The experiments on the standard dataset demonstrate that the proposed algorithm achieves better results than the state-of-the-art algorithms.