Sketch-QNet: A Quadruplet ConvNet for Color Sketch-based Image Retrieval
Anibal Fuentes, Jose M. Saavedra

TL;DR
Sketch-QNet introduces a quadruplet convolutional network that improves color sketch-based image retrieval by better discriminating weakly relevant items, achieving state-of-the-art results in the field.
Contribution
The paper proposes a novel quadruplet network architecture, Sketch-QNet, specifically designed for color sketch-based image retrieval, addressing limitations of triplet-based methods.
Findings
Achieves state-of-the-art performance on CSBIR tasks.
Effectively discriminates weakly relevant items in image retrieval.
Outperforms existing triplet-based architectures.
Abstract
Architectures based on siamese networks with triplet loss have shown outstanding performance on the image-based similarity search problem. This approach attempts to discriminate between positive (relevant) and negative (irrelevant) items. However, it undergoes a critical weakness. Given a query, it cannot discriminate weakly relevant items, for instance, items of the same type but different color or texture as the given query, which could be a serious limitation for many real-world search applications. Therefore, in this work, we present a quadruplet-based architecture that overcomes the aforementioned weakness. Moreover, we present an instance of this quadruplet network, which we call Sketch-QNet, to deal with the color sketch-based image retrieval (CSBIR) problem, achieving new state-of-the-art results.
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Taxonomy
MethodsTriplet Loss
