| dc.description.abstract | Scene Recognition is an important operation of Image Semantic Segmentation, in the wide range of scene recognition, it is a thorny issue to correctly and efficient find effective location information in specific scene. In the mission of scene recognition, a scene is mainly comprised of three elements, including object, spatial layout and the relationship between backgrounds, these object types in scene have huge impact on results of classification. Through this matter, scene could be recognized based on those identified objects of scene, for example, bathtub or toilet in the bathroom, bed or writing desk in the bedroom.
In this thesis, an effective architecture for scene recognition is proposed. The architecture includes a pre-process step to identify feature of each object, then classify specified scene based on the results of object feature. Moreover, those input pictures will be pre-processed through Mask R-CNN algorithm to identify specific indoor objects by results of segmentation, and those specified indoor objects become elements for scene recognition classification. The experimental results show that through pre-process of object identification, the proposed method has the advantages of accuracy in scene recognition.
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