4 research outputs found
An Efficient Approximate kNN Graph Method for Diffusion on Image Retrieval
The application of the diffusion in many computer vision and artificial
intelligence projects has been shown to give excellent improvements in
performance. One of the main bottlenecks of this technique is the quadratic
growth of the kNN graph size due to the high-quantity of new connections
between nodes in the graph, resulting in long computation times. Several
strategies have been proposed to address this, but none are effective and
efficient. Our novel technique, based on LSH projections, obtains the same
performance as the exact kNN graph after diffusion, but in less time
(approximately 18 times faster on a dataset of a hundred thousand images). The
proposed method was validated and compared with other state-of-the-art on
several public image datasets, including Oxford5k, Paris6k, and Oxford105k
Deep Image Retrieval: A Survey
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 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 instance-based CBIR.Comment: 20 pages, 11 figure