This study aims at the great limitations caused by the non-ROI (region of interest) information interference in traditional scene classification algorithms, including the changes of multiscale or various visual angles and the high similarity between classes and other factors. An effective indoor scene classification mechanism based on multiple descriptors fusion is proposed, which introduces the depth images to improve descriptor efficiency. The greedy descriptor filter algorithm (GDFA) is proposed to obtain valuable descriptors, and the multiple descriptor combination method is also given to further improve descriptor performance. Performance analysis and simulation results show that multiple descriptors fusion not only can achieve higher classification accuracy than principal components analysis (PCA) in the condition with medium and large size of descriptors but also can improve the classification accuracy than the other existing algorithms effectively.

中文翻译：

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基于多描述符融合的室内场景分类机制研究

这项研究针对传统场景分类算法中非ROI（感兴趣区域）信息干扰造成的巨大局限性，包括多尺度或各种视角的变化以及类与其他因素之间的高度相似性。提出了一种基于多描述符融合的有效室内场景分类机制，引入深度图像以提高描述符的效率。提出了贪婪描述符过滤算法（GDFA）来获取有价值的描述符，并提出了多描述符组合方法以进一步提高描述符的性能。