NDLI: Bow image retrieval method based on SSD target detection

Content Provider	IET Digital Library
Author	Liao, Kaiyang Fan, Bing Zheng, Yuanlin Lin, Guangfeng Cao, Congjun
Abstract	The query image is usually a simple and single object in image retrieval, and the reference images in the database usually have many distractions. The precision of image retrieval can be greatly improved If the target regions in the database image are extracted during retrieval. So this paper proposes a Bow image retrieval method based on SSD target detection. First, the training gallery is manually annotated to record the location and size information. Second, the SSD target detection model is trained with the labeled training gallery to obtain the target object SSD model. Third, the SSD model is used to locate the similar target regions of the reference image and the query graph. Finally, the target region information is mapped into the convolutional features, and these feature vectors are used for image similarity matching. The performance of the proposed method is evaluated on Paris6k, Oxford5k, Paris106k and Oxford105k databases. The experimental results show that the accuracy of image retrieval will be greatly improved by adding optimization methods in the proposed image retrieval framework. The image retrieval accuracy of this method is higher than that of similar methods in recent years.
Starting Page	4441
Ending Page	4449
Page Count	9
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 17, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2020.0478
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/17
Journal	IET Image Processing
Publisher Date	2020-12-04
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	256-dimensional Features 512-dimensional Vector Characterisation Images Bow Image Retrieval Method Computer Vision And Image Processing Technique Convolution Features Convolutional Features Convolutional Neural Nets Database Image Feature Extraction Image Classification Image Matching Image Recognition Image Representation Image Retrieval Image Retrieval Accuracy Image Retrieval Framework Image Similarity Information Retrieval Technique Labelled Training Gallery Object Detection Paris106k Paris5k Paris6k Query Expansion Rearrangement Method Query Image Recent Optimal CroW Method Reference Image Retrieval Mean Average Precision Similar Target Region Single-shot MultiBox Detector Target Detection SSD Target Detection Model Target Object SSD Model Target Region Information Vector
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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