Dual-stream multi-layer cross encoding network for texture analysis of architectural heritage elements
Source abstract
Abstract
Texture provides valuable insights into building materials, structure, style, and historical context. However, traditional deep learning features struggle to address architectural textures due to complex inter-class similarities and intra-class variations. To overcome these challenges, this paper proposes a Dual-stream Multi-layer Cross Encoding Network (DMCE-Net). DMCE-Net treats deep feature maps from different layers as experts, each focusing on specific texture attributes. It includes two complementary encoding streams: the intra-layer encoding stream efficiently captures diverse texture perspectives from individual layers through multi-attribute joint encoding, while the inter-layer encoding stream facilitates mutual interaction and knowledge integration across layers using a cross-layer binary encoding mechanism. By leveraging collaborative interactions between both streams, DMCE-Net effectively models and represents complex texture attributes of architectural heritage elements. Extensive experimental evaluations on architectural heritage datasets and three texture databases demonstrate that DMCE-Net achieves superior performance compared to existing deep learning methods and handcrafted features, providing reliable texture representations.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Image Retrieval and Classification Techniques · 3D Surveying and Cultural Heritage · Image Processing and 3D Reconstruction
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (59)
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