Sketch-based image retrieval with deep visual semantic descriptor

作者：

Highlights：

• Novel Real-time SBIR is built by employing multimodal deep sketch-image matching.

• Deep Visual Semantic Descriptor is created to bridge sketch-image multimodal gap.

• Sketch-like Transformation is established to improve sketch-image resemblance.

• Re-ranking Optimization is introduced to characterize sketch-image correlation.

摘要

•Novel Real-time SBIR is built by employing multimodal deep sketch-image matching.•Deep Visual Semantic Descriptor is created to bridge sketch-image multimodal gap.•Sketch-like Transformation is established to improve sketch-image resemblance.•Re-ranking Optimization is introduced to characterize sketch-image correlation.

论文关键词：Sketch-based image retrieval (SBIR),Deep learning,Deep visual semantic descriptor,Sketch-like transformation,Re-ranking optimization,Multiple feature fusion,Accelerated hierarchical K-means clustering

论文评审过程：Received 5 February 2017, Revised 21 August 2017, Accepted 30 November 2017, Available online 1 December 2017, Version of Record 21 December 2017.

论文官网地址：https://doi.org/10.1016/j.patcog.2017.11.032