City Architectural Color Recognition Based on Deep Learning and Pattern Recognition

Abstract

The collection of information about buildings and their colors is an important aspect of urban planning. The intelligent recognition of buildings using image information plays a significant role in the development of smart cities and urban planning. This thesis proposes a building color-recognition technique based on morphological features utilizing convolutional neural networks and the K-means clustering algorithm of image-recognition technology. The proposed method can identify buildings in images and classify them into two categories, buildings and panoramas, for color extraction and matching. This method involves training convolutional neural networks on deep learning so that the buildings can be differentiated and segmented. Subsequently, the K-means algorithm extracts colors from the segmented building images. The extracted building category, color, and text information were analyzed to obtain a comparison and analysis results of buildings and panoramas. The results demonstrated that the system is capable of accurately segmenting buildings, as well as extracting colors from both buildings and panoramas separately. It can also contribute to the extraction and presentation of color schemes in smart city planning and provide valuable insights for the future development of urban colors.