Collections of bronze inscription images are increasing rapidly. To use these
images efficiently, we proposed an effective content-based image retrieval
framework using deep learning. Specifically, we extract discriminative local
features for image retrieval using the activations of the convolutional neural
network and binarize the extracted features for improving the efficiency of
image retrieval, firstly. Then, we use the cosine metric and Euclidean metric to
calculate the similarity between the query image and dataset images. The result
shows that the proposed framework has an impressive accuracy.
BabenkoA, BabenkoA, SlesarevA, et al. (2014) Neural Codes for
Image Retrieval. In: Fleet D, et al (eds) Computer
Vision- ECCV 2014. Cham: Springer, pp.
584–599.
2.
EakinsJ (2002) Towards intelligent image
retrieval. Pattern
Recognition. 35(1):
3–14.
3.
GudivadaN and RaghavanV (1995) Design and evaluation of algorithms
for image retrieval by spatial similarity.
ACM Transactions on Information Systems
(TOIS)13( 2):
115–144.
4.
GudivadaN and RaghavanV (2002) Content based image retrieval
systems. Computer28( 9):
18–22.
5.
HamrerasS, BouchehamB, Molina-CabelloM, et al. (2019) Content based
image retrieval by convolutional neural networks.
In: Ferrández Vicente J., Álvarez-Sánchez J.,
de la Paz López F., Toledo Moreo J., Adeli H. (eds) From
Bioinspired Systems and Biomedical Applications to Machine
Learning. Cham: Springer, pp.277–286.
6.
HouR, ChenC and ShahM (2017) Tube Convolutional Neural Network
(T-CNN) for Action Detection in Videos. In
Proceedings of the IEEE international conference on computer
vision, Venice, Italy, 22-29 October 2017, pp.
5822–5831.
7.
JainA and VailayaA (1996) Image retrieval using color and
shape. Pattern Recognition29( 8):
1233–1244.
8.
JégouH, PerronninF, DouzeM, et al. (2012) Aggregating
local image descriptors into compact codes.
IEEE Transactions on Pattern Analysis and Machine
Intelligence34(9):
1704–1716.
9.
KumarKK and GopalTV (2014) A novel approach to self order
feature reweighting in CBIR to reduce semantic gap using Relevance
Feedback. In: 2014 International Conference on
Circuits, Power and Computing Technologies [ICCPCT-2014] Nagercoil,
Tamil Nadu, India, 20-21 March 2016, pp.
1437–1442.
10.
LecunY and BottouL (1998) Gradient-based learning applied to
document recognition. Proceedings of the
IEEE86( 11):
2278–2324.
11.
LinK, YangF, HsiaoH, et al. (2015) Deep learning of
binary hash codes for fast image retrieval. In:
2015 IEEE Conference on Computer Vision and Pattern
Recognition Workshops (CVPRW), Boston, MA,
USA, 7-12 June 2015, pp. 27–35.
12.
LiuY, ZhangD, LuG, et al. (2007) A survey of
content-based image retrieval with high-level semantics.
Pattern Recognition40( 1):
262–282.
13.
LoweD (1999) Object recognition from local
scale-invariant features. In: Proceedings of
the seventh IEEE international conference on computer vision,
Kerkyra, Greece, 20-27 September 1999, pp.
1150–1157.
14.
ManjunathS and MaY(1996) Texture features for browsing and
retrieval of image data. IEEE Transactions
on Pattern Analysis and Machine Intelligence18( 8):
837–842.
15.
NorouziM, FleetD and SalakhutdinovR (2012) Hamming distance metric
learning. In: Proceedings of the 25th
International Conference on Neural Information Processing Systems -
Volume 1 (NIPS’12). Red Hook: Curran Associates
Inc., pp. 1061–1069.
16.
YangJ, JiangYG, HauptmannAG and NgoCW (2007) Evaluating bag-of-visual-words representations in
scene classification. In: Proceedings of the 9th ACM SIGMM International
Workshop on Multimedia Information Retrieval, MIR 2007, Augsburg,
Bavaria, Germany, September 24-29, 2007, pp.
197–206.
17.
ZhangBC, GuJX, ChenC, et al. (2018) One-two-one
networks for compression artifacts reduction in remote
sensing. ISPRS Journal of Photogrammetry and
Remote Sensing ( 145 Part A):
184–196.
