Research on knowledge extraction from street scene images based on hy-brid intelligence
This study presents a hybrid intelligence-based approach,named K-CAPSNet,for extracting knowledge from streetscape images.To tackle the challenge of intelligent extraction of streetscape image objects,we develop a panoramic seg-mentation network with a joint attention mechanism that integrates both channel information and spatial information of streetscape images.This improves the object segmentation accuracy.Additionally,we incorporate streetscape knowledge,which is formed by people in production and life,into the streetscape image cognition process.We set the object marking threshold using a priori knowledge to optimize the segmentation results.Moreover,we utilize the a priori knowledge of streetscape images to verify the topological relationship between streetscape objects and to mine spatial relationship knowledge using depth information.Finally,we employ semantic templates to describe and express the type,number,and spatial rela-tionship between streetscape objects.The experimental results demonstrate that our method outperforms the baseline network and significantly improves the quality of panoramic segmentation and recognition,thereby achieving better extraction and ex-pression of the knowledge of streetscape images.
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