Learning visual similarity for product design with convolutional neural networks
Learning visual similarity for product design with convolutional neural networks
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DOI：
10.1145/2766959
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发表时间：
2015-07
期刊：
影响因子：
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通讯作者：
Sean Bell;Kavita Bala
中科院分区：
文献类型：
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作者：
Sean Bell;Kavita Bala
流行的网站，如Houzz、Pinterest和LikeThatDecor，都有用户社区相互帮助回答关于图片产品的问题。在这篇文章中，我们学习了视觉搜索在室内设计中的嵌入。我们的嵌入包含两个不同的产品图像域：从互联网场景中裁剪的产品，以及其标志性形式的产品。通过这种多领域的嵌入，我们展示了视觉搜索的几个应用，包括在场景中识别产品和寻找风格相似的产品。为了获得嵌入，我们对图像对训练卷积神经网络。我们探索了几种训练体系结构，包括重新调整对象分类器的用途，使用暹罗网络和使用多任务学习。我们对我们的搜索进行定量和定性的评估，并展示跨多个视觉领域的高质量搜索结果，从而在室内设计中实现新的应用。
Popular sites like Houzz, Pinterest, and LikeThatDecor, have communities of users helping each other answer questions about products in images. In this paper we learn an embedding for visual search in interior design. Our embedding contains two different domains of product images: products cropped from internet scenes, and products in their iconic form. With such a multi-domain embedding, we demonstrate several applications of visual search including identifying products in scenes and finding stylistically similar products. To obtain the embedding, we train a convolutional neural network on pairs of images. We explore several training architectures including re-purposing object classifiers, using siamese networks, and using multitask learning. We evaluate our search quantitatively and qualitatively and demonstrate high quality results for search across multiple visual domains, enabling new applications in interior design.