Abstract: In recent years a vast amount of visual content has been generated and shared
from various fields, such as social media platforms, medical images, and
robotics. This abundance of content creation and sharing has introduced new
challenges. In particular, searching databases for similar content, i.e.
content based image retrieval (CBIR), is a long-established research area, and
more efficient and accurate methods are needed for real time retrieval.
Artificial intelligence has made progress in CBIR and has significantly
facilitated the process of intelligent search. In this survey we organize and
review recent CBIR works that are developed based on deep learning algorithms
and techniques, including insights and techniques from recent papers. We
identify and present the commonly-used databases, benchmarks, and evaluation
methods used in the field. We collect common challenges and propose promising
future directions. More specifically, we focus on image retrieval with deep
learning and organize the state of the art methods according to the types of
deep network structure, deep features, feature enhancement methods, and network
fine-tuning strategies. Our survey considers a wide variety of recent methods,
aiming to promote a global view of the field of category-based CBIR.