Supervised hashing binary code with deep CNN for image retrieval

2015 ◽  
Author(s):  
Jun-yi Li ◽  
Jian-hua Li
Keyword(s):  
Image Retrieval ◽  
Binary Code ◽  
Deep Cnn

scholarly journals Deep Learning Triplet Ordinal Relation Preserving Binary Code for Remote Sensing Image Retrieval Task

2021 ◽  
Vol 13 (23) ◽  
pp. 4786
Author(s):  
Zhen Wang ◽  
Nannan Wu ◽  
Xiaohan Yang ◽  
Bingqi Yan ◽  
Pingping Liu
Keyword(s):  
Remote Sensing ◽  
Image Retrieval ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Binary Code ◽  
Satellite Observation ◽  
Cross Entropy ◽  
Retrieval Task ◽  
Search Tasks ◽  
Low Dimensional

As satellite observation technology rapidly develops, the number of remote sensing (RS) images dramatically increases, and this leads RS image retrieval tasks to be more challenging in terms of speed and accuracy. Recently, an increasing number of researchers have turned their attention to this issue, as well as hashing algorithms, which map real-valued data onto a low-dimensional Hamming space and have been widely utilized to respond quickly to large-scale RS image search tasks. However, most existing hashing algorithms only emphasize preserving point-wise or pair-wise similarity, which may lead to an inferior approximate nearest neighbor (ANN) search result. To fix this problem, we propose a novel triplet ordinal cross entropy hashing (TOCEH). In TOCEH, to enhance the ability of preserving the ranking orders in different spaces, we establish a tensor graph representing the Euclidean triplet ordinal relationship among RS images and minimize the cross entropy between the probability distribution of the established Euclidean similarity graph and that of the Hamming triplet ordinal relation with the given binary code. During the training process, to avoid the non-deterministic polynomial (NP) hard problem, we utilize a continuous function instead of the discrete encoding process. Furthermore, we design a quantization objective function based on the principle of preserving triplet ordinal relation to minimize the loss caused by the continuous relaxation procedure. The comparative RS image retrieval experiments are conducted on three publicly available datasets, including UC Merced Land Use Dataset (UCMD), SAT-4 and SAT-6. The experimental results show that the proposed TOCEH algorithm outperforms many existing hashing algorithms in RS image retrieval tasks.

Multi-trend binary code descriptor: a novel local texture feature descriptor for image retrieval

2017 ◽  
Vol 12 (2) ◽  
pp. 247-254 ◽  
Author(s):  
Laihang Yu ◽  
Lin Feng ◽  
Huibing Wang ◽  
Li Li ◽  
Yang Liu ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Binary Code ◽  
Texture Feature ◽  
Feature Descriptor ◽  
Local Texture

scholarly journals Online Hashing with Efficient Updating of Binary Codes

2020 ◽  
Vol 34 (07) ◽  
pp. 12354-12361
Author(s):  
Zhenyu Weng ◽  
Yuesheng Zhu
Keyword(s):  
Image Retrieval ◽  
Binary Code ◽  
Hash Functions ◽  
Image Databases ◽  
Streaming Data ◽  
Binary Codes ◽  
Retrieval Process ◽  
Retrieval Accuracy ◽  
Projection Functions

Online hashing methods are efficient in learning the hash functions from the streaming data. However, when the hash functions change, the binary codes for the database have to be recomputed to guarantee the retrieval accuracy. Recomputing the binary codes by accumulating the whole database brings a timeliness challenge to the online retrieval process. In this paper, we propose a novel online hashing framework to update the binary codes efficiently without accumulating the whole database. In our framework, the hash functions are fixed and the projection functions are introduced to learn online from the streaming data. Therefore, inefficient updating of the binary codes by accumulating the whole database can be transformed to efficient updating of the binary codes by projecting the binary codes into another binary space. The queries and the binary code database are projected asymmetrically to further improve the retrieval accuracy. The experiments on two multi-label image databases demonstrate the effectiveness and the efficiency of our method for multi-label image retrieval.

Deep Hashing with Triplet Labels and Unification Binary Code Selection for Fast Image Retrieval

2018 ◽  
pp. 277-288
Author(s):  
Chang Zhou ◽  
Lai-Man Po ◽  
Mengyang Liu ◽  
Wilson Y. F. Yuen ◽  
Peter H. W. Wong ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Binary Code ◽  
Deep Hashing ◽  
Selection For

Deep CNN Combined With Relevance Feedback for Trademark Image Retrieval

2018 ◽  
Vol 29 (1) ◽  
pp. 894-909 ◽  
Author(s):  
Latika Pinjarkar ◽  
Manisha Sharma ◽  
Smita Selot
Keyword(s):  
Image Retrieval ◽  
Search Space ◽  
Feedback Mechanism ◽  
Self Organizing Map ◽  
Deep Convolutional Neural Networks ◽  
Feedback Information ◽  
Feature Representations ◽  
Significant Performance ◽  
Deep Cnn ◽  
Performance Results

Abstract Trademark recognition and retrieval is a vital appliance component of content-based image retrieval (CBIR). Reduction in the semantic gap, attaining more accuracy, reduction in computation complexity, and hence in execution time, are the major challenges in designing and developing a trademark retrieval system. The direction of the proposed work takes into account these challenges by implementing trademark image retrieval through deep convolutional neural networks (DCNNs) integrated with a relevant feedback mechanism. The dataset features are optimized through particle swarm optimization (PSO), reducing the search space. These best/optimized features are given to the self-organizing map (SOM) for clustering at the preprocessing stage. The CNN model is trained on feature representations of relevant and irrelevant images, using the feedback information from the user bringing the marked relevant images closer to the query. Experimentation proved a significant performance when evaluated using FlickrLogos-27, FlickrLogos-32, and FlickrLogos-32 PLUS datasets, as illustrated in the performance results section.

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