scholarly journals Fabric Image Retrieval System Using Hierarchical Search Based on Deep Convolutional Neural Network

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 35405-35417 ◽  
Author(s):  
Jun Xiang ◽  
Ning Zhang ◽  
Ruru Pan ◽  
Weidong Gao
Keyword(s):  
Neural Network ◽  
Image Retrieval ◽  
Convolutional Neural Network ◽  
Retrieval System ◽  
Deep Convolutional Neural Network ◽  
Hierarchical Search ◽  
Image Retrieval System

Image Retrieval System based on a Binary Auto-Encoder and a Convolutional Neural Network

2020 ◽  
Vol 18 (11) ◽  
pp. 1925-1932
Author(s):  
Ferreyra-Ramirez Andres ◽  
Rodriguez-Martinez Eduardo ◽  
Aviles-Cruz Carlos ◽  
Lopez-Saca Fidel
Keyword(s):  
Neural Network ◽  
Image Retrieval ◽  
Convolutional Neural Network ◽  
Retrieval System ◽  
Image Retrieval System

scholarly journals Locality Constrained Deep Supervised Hashing for Image Retrieval

2017 ◽  
Author(s):  
Hao Zhu ◽  
Shenghua Gao
Keyword(s):  
Neural Network ◽  
Image Retrieval ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Quantization Error ◽  
Deep Convolutional Neural Network ◽  
Training Phase ◽  
Deep Hashing ◽  
Image Pairs ◽  
Hash Codes

Deep Convolutional Neural Network (DCNN) based deep hashing has shown its success for fast and accurate image retrieval, however directly minimizing the quantization error in deep hashing will change the distribution of DCNN features, and consequently change the similarity between the query and the retrieved images in hashing. In this paper, we propose a novel Locality-Constrained Deep Supervised Hashing. By simultaneously learning discriminative DCNN features and preserving the similarity between image pairs, the hash codes of our scheme preserves the distribution of DCNN features thus favors the accurate image retrieval.The contributions of this paper are two-fold: i) Our analysis shows that minimizing quantization error in deep hashing makes the features less discriminative which is not desirable for image retrieval; ii) We propose a Locality-Constrained Deep Supervised Hashing which preserves the similarity between image pairs in hashing.Extensive experiments on the CIFARA-10 and NUS-WIDE datasets show that our method significantly boosts the accuracy of image retrieval, especially on the CIFAR-10 dataset, the improvement is usually more than 6% in terms of the MAP measurement. Further, our method demonstrates 10 times faster than state-of-the-art methods in the training phase.

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