双分支多粒度局部对齐的实例级草图图像检索

doi:10.16451/j.cnki.issn1003-6059.202308003

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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.

Key words： Sketch-Based Image Retrieval Feature Extraction Feature Fusion Cross-Modal Retrieval

Received: 08 June 2023

ZTFLH:

TP389.1

Fund:National Key Research and Development Program of China(No.2022YFB3104700), National Natural Science Foundation of China(No.61976158,61976160,62076182)

Corresponding Authors: MIAO Duoqian, Ph.D., professor. His research interests include machine learning, data mining, big data analysis, granular computing, artificial intelligence and text image processing.

About author:: HAN Xuekun, master student. His research interests include image retrieval, sketch recognition and machine learning.ZHANG Hongyun, Ph.D., associate professor. Her research interests include principal curve algorithm, granular computing and fuzzy sets.Zhang Qixian, Ph.D. candidate. His research interests include person search, computer vision and object detection.

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Cite this article:

HAN Xuekun,MIAO Duoqian,ZHANG Hongyun等. Instance-Level Sketch-Based Image Retrieval Based on Two Stream Multi-granularity Local Alignment Network[J]. Pattern Recognition and Artificial Intelligence, 2023, 36(8): 701-711.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202308003 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2023/V36/I8/701

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