Instance-Level Sketch-Based Image Retrieval Based on Two Stream Multi-granularity Local Alignment Network
HAN Xuekun1,2, MIAO Duoqian1,2, ZHANG Hongyun1,2, ZHANG Qixian1,2
1. College of Electronic and Information Engineering, Tongji University, Shanghai 201804; 2. Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai 201804
Abstract：The goal of instance-level sketch-based image retrieval is to retrieve images by sketches. There is a significant modality gap and feature misalignment issue between sketches and images. In the existing methods, the modality gap between sketches and images cannot be effectively reduced, and only information at a single granularity is captured. Thus, features cannot be aligned effectively. To address these issues, a two stream multi-granularity local alignment network(TSMLA) is proposed. A two-stream feature extractor is introduced to extract both modality-shared and modality-specific local features. These features are simultaneously utilized to calculate the distance between the sketch and the image and reduce the differences between different modalities. Moreover, a multi-granularity local alignment module is adopted to pool the distance matrix at various granularities. Local features are aligned at different scales to effectively address the problem of feature misalignment. TSMLA can fully utilize the information of sketches and real images, while effectively utilizing the connections between features of different granularities. Experiments on multiple datasets validate the effectiveness of TSMLA.
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2023, Vol. 36 
