360 research outputs found
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
A deep locality-sensitive hashing approach for achieving optimal ‎image retrieval satisfaction
Efficient methods that enable high and rapid image retrieval are continuously needed, especially with the large mass of images that are generated from different sectors and domains like business, communication media, and entertainment. Recently, deep neural networks are extensively proved higher-performing models compared to other traditional models. Besides, combining hashing methods with a deep learning architecture improves the image retrieval time and accuracy. In this paper, we propose a novel image retrieval method that employs locality-sensitive hashing with convolutional neural networks (CNN) to extract different types of features from different model layers. The aim of this hybrid framework is focusing on both the high-level information that provides semantic content and the low-level information that provides visual content of the images. Hash tables are constructed from the extracted features and trained to achieve fast image retrieval. To verify the effectiveness of the proposed framework, a variety of experiments and computational performance analysis are carried out on the CIFRA-10 and NUS-WIDE datasets. The experimental results show that the proposed method surpasses most existing hash-based image retrieval methods
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