论文标题
使用深度学习对基于内容的图像检索的十年调查
A Decade Survey of Content Based Image Retrieval using Deep Learning
论文作者
论文摘要
基于内容的图像检索旨在根据查询图像从大型数据集中找到相似的图像。通常，查询图像的代表特征和数据集图像之间的相似性用于对图像进行排名。在早期，已经根据表示图像的颜色，纹理，形状等等视觉提示进行了各种设计的功能描述符。但是，深度学习已成为十年来手工设计的功能工程的主要替代方案。它从数据中自动学习功能。本文对过去十年中基于内容的图像检索进行了一项对基于深度学习的发展的全面调查。还从不同角度对现有最新方法的分类进行了分类，以便对进度进行更多了解。该调查中使用的分类法涵盖了不同的监督，不同的网络，不同的描述符类型和不同的检索类型。还使用最新方法进行了绩效分析。还提供了见解，以使研究人员的利益观察进步并做出最佳选择。本文提出的调查将有助于进一步研究图像检索的进步。
The content based image retrieval aims to find the similar images from a large scale dataset against a query image. Generally, the similarity between the representative features of the query image and dataset images is used to rank the images for retrieval. In early days, various hand designed feature descriptors have been investigated based on the visual cues such as color, texture, shape, etc. that represent the images. However, the deep learning has emerged as a dominating alternative of hand-designed feature engineering from a decade. It learns the features automatically from the data. This paper presents a comprehensive survey of deep learning based developments in the past decade for content based image retrieval. The categorization of existing state-of-the-art methods from different perspectives is also performed for greater understanding of the progress. The taxonomy used in this survey covers different supervision, different networks, different descriptor type and different retrieval type. A performance analysis is also performed using the state-of-the-art methods. The insights are also presented for the benefit of the researchers to observe the progress and to make the best choices. The survey presented in this paper will help in further research progress in image retrieval using deep learning.
