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Zero-Shot Sketch-Based Image Retrieval Using StyleGen and Stacked Siamese Neural Networks.

Venkata Rama Muni Kumar Gopu¹, Madhavi Dunna¹

¹Department of Electrical, Electronics and Communication Engineering (EECE), Gitam School of Technology, Gitam Deemed to be University, Rushikonda, Visakhapatnam 530045, India.

Journal of Imaging

|April 26, 2024

Summary

This study introduces StyleGen, a novel method for sketch-based image retrieval (SBIR). StyleGen generates realistic images from sketches, significantly improving retrieval accuracy in zero-shot scenarios by bridging the domain gap.

Keywords:

SSiNN ZS-SBIR domain gap sketch-based image retrieval stacked Siamese neural network

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Area of Science:

Computer Vision
Machine Learning
Artificial Intelligence

Background:

Sketch-based image retrieval (SBIR) is challenging due to the domain gap between ambiguous sketches and natural images.
Zero-shot SBIR requires retrieving images from classes not seen during model training, exacerbating the domain gap.

Purpose of the Study:

To propose StyleGen, an elegant retrieval methodology for generating candidate images that match the domain of repository images.
To reduce the domain gap in sketch-based image retrieval tasks.

Main Methods:

Utilizes a two-stage neural network architecture: the stacked Siamese network.
Employs StyleGen to generate fake candidate images aligned with the target image domain.

Main Results:

Demonstrates marked performance improvement on TU-Berlin Extended and Sketchy Extended datasets.
Achieves superior results compared to current state-of-the-art approaches in SBIR.
Maintains generalizability of the retrieval approach.

Conclusions:

StyleGen effectively bridges the domain gap in SBIR.
The stacked Siamese network architecture provides outstanding retrieval performance.
The proposed methodology significantly advances zero-shot SBIR capabilities.