Deep Learning to Ternary Hash Codes by Continuation

10.36227/techrxiv.17083019.v1 ◽

2021 ◽

Author(s):

Mingrui Chen ◽

Weiyu Li ◽

weizhi lu

Keyword(s):

Deep Learning ◽

Image Retrieval ◽

Continuation Method ◽

Binary Codes ◽

Hard Thresholding ◽

Joint Learning ◽

Ternary Codes ◽

First Time ◽

Hash Codes ◽

Discrete Functions

Recently, it has been observed that $\{0,\pm1\}$-ternary codes which are simply generated from deep features by hard thresholding, tend to outperform $\{-1, 1\}$-binary codes in image retrieval. To obtain better ternary codes, we for the first time propose to jointly learn the features with the codes by appending a smoothed function to the networks. During training, the function could evolve into a non-smoothed ternary function by a continuation method, and then generate ternary codes. The method circumvents the difficulty of directly training discrete functions and reduces the quantization errors of ternary codes. Experiments show that the proposed joint learning indeed could produce better ternary codes.

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Deep learning of binary hash codes for fast image retrieval

2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) ◽

10.1109/cvprw.2015.7301269 ◽

2015 ◽

Cited By ~ 216

Author(s):

Kevin Lin ◽

Huei-Fang Yang ◽

Jen-Hao Hsiao ◽

Chu-Song Chen

Keyword(s):

Deep Learning ◽

Image Retrieval ◽

Hash Codes

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Rapid image retrieval with binary hash codes based on deep learning

Third International Workshop on Pattern Recognition ◽

10.1117/12.2502072 ◽

2018 ◽

Author(s):

邓广伟 ◽

Cheng Xu ◽

XiaoHan Tu ◽

Tao Li ◽

Nan Gao

Keyword(s):

Deep Learning ◽

Image Retrieval ◽

Hash Codes

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Applications of Deep Learning Using Quaternary Hash Codes for Image Retrieval

2019 15th International Conference on Semantics, Knowledge and Grids (SKG) ◽

10.1109/skg49510.2019.00018 ◽

2019 ◽

Author(s):

Han Yu ◽

Meijun Shen ◽

Jingjing Hang ◽

Bin Wu

Keyword(s):

Deep Learning ◽

Image Retrieval ◽

Hash Codes

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Semantic Segmentation of Satellite Images: A Deep Learning Approach Integrated with Geospatial Hash Codes

Remote Sensing ◽

10.3390/rs13142723 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2723

Author(s):

Naisen Yang ◽

Hong Tang

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Deep Neural Network ◽

Satellite Images ◽

Spatial Information ◽

Semantic Segmentation ◽

Binary Codes ◽

Underlying Assumption ◽

Hash Codes

Satellite images are always partitioned into regular patches with smaller sizes and then individually fed into deep neural networks (DNNs) for semantic segmentation. The underlying assumption is that these images are independent of one another in terms of geographic spatial information. However, it is well known that many land-cover or land-use categories share common regional characteristics within a certain spatial scale. For example, the style of buildings may change from one city or country to another. In this paper, we explore some deep learning approaches integrated with geospatial hash codes to improve the semantic segmentation results of satellite images. Specifically, the geographic coordinates of satellite images are encoded into a string of binary codes using the geohash method. Then, the binary codes of the geographic coordinates are fed into the deep neural network using three different methods in order to enhance the semantic segmentation ability of the deep neural network for satellite images. Experiments on three datasets demonstrate the effectiveness of embedding geographic coordinates into the neural networks. Our method yields a significant improvement over previous methods that do not use geospatial information.

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Probability Ordinal-Preserving Semantic Hashing for Large-Scale Image Retrieval

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3442204 ◽

2021 ◽

Vol 15 (3) ◽

pp. 1-22

Author(s):

Zheng Zhang ◽

Xiaofeng Zhu ◽

Guangming Lu ◽

Yudong Zhang

Keyword(s):

Image Retrieval ◽

Large Scale ◽

Original Data ◽

Learning Framework ◽

Space Transformation ◽

Similarity Preserving ◽

Large Scale Image Retrieval ◽

Asymmetric Graph ◽

First Time ◽

Hash Codes

Semantic hashing enables computation and memory-efficient image retrieval through learning similarity-preserving binary representations. Most existing hashing methods mainly focus on preserving the piecewise class information or pairwise correlations of samples into the learned binary codes while failing to capture the mutual triplet-level ordinal structure in similarity preservation. In this article, we propose a novel Probability Ordinal-preserving Semantic Hashing (POSH) framework, which for the first time defines the ordinal-preserving hashing concept under a non-parametric Bayesian theory. Specifically, we derive the whole learning framework of the ordinal similarity-preserving hashing based on the maximum posteriori estimation, where the probabilistic ordinal similarity preservation, probabilistic quantization function, and probabilistic semantic-preserving function are jointly considered into one unified learning framework. In particular, the proposed triplet-ordering correlation preservation scheme can effectively improve the interpretation of the learned hash codes under an economical anchor-induced asymmetric graph learning model. Moreover, the sparsity-guided selective quantization function is designed to minimize the loss of space transformation, and the regressive semantic function is explored to promote the flexibility of the formulated semantics in hash code learning. The final joint learning objective is formulated to concurrently preserve the ordinal locality of original data and explore potentials of semantics for producing discriminative hash codes. Importantly, an efficient alternating optimization algorithm with the strictly proof convergence guarantee is developed to solve the resulting objective problem. Extensive experiments on several large-scale datasets validate the superiority of the proposed method against state-of-the-art hashing-based retrieval methods.

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An Anatomy of a Hybrid Color Descriptor with a Neural Network Model to Enhance the Retrieval Accuracy of an Image Retrieval System

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813666191122113801 ◽

2019 ◽

Vol 13 ◽

Author(s):

Shikha Bhardwaj ◽

Gitanjali Pandove ◽

Pawan Kumar Dahiya

Keyword(s):

Neural Network ◽

Deep Learning ◽

Image Retrieval ◽

Hybrid System ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Retrieval Accuracy ◽

Color Descriptor ◽

Benchmark Datasets ◽

Color Moment

Background: In order to retrieve a particular image from vast repository of images, an efficient system is required and such an eminent system is well-known by the name Content-based image retrieval (CBIR) system. Color is indeed an important attribute of an image and the proposed system consist of a hybrid color descriptor which is used for color feature extraction. Deep learning, has gained a prominent importance in the current era. So, the performance of this fusion based color descriptor is also analyzed in the presence of Deep learning classifiers. Method: This paper describes a comparative experimental analysis on various color descriptors and the best two are chosen to form an efficient color based hybrid system denoted as combined color moment-color autocorrelogram (Co-CMCAC). Then, to increase the retrieval accuracy of the hybrid system, a Cascade forward back propagation neural network (CFBPNN) is used. The classification accuracy obtained by using CFBPNN is also compared to Patternnet neural network. Results: The results of the hybrid color descriptor depict that the proposed system has superior results of the order of 95.4%, 88.2%, 84.4% and 96.05% on Corel-1K, Corel-5K, Corel-10K and Oxford flower benchmark datasets respectively as compared to many state-of-the-art related techniques. Conclusion: This paper depict an experimental and analytical analysis on different color feature descriptors namely, Color moment (CM), Color auto-correlogram (CAC), Color histogram (CH), Color coherence vector (CCV) and Dominant color descriptor (DCD). The proposed hybrid color descriptor (Co-CMCAC) is utilized for the withdrawal of color features with Cascade forward back propagation neural network (CFBPNN) is used as a classifier on four benchmark datasets namely Corel-1K, Corel-5K and Corel-10K and Oxford flower.

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