Improved Deep Hashing with Scalable Interblock for Tourist Image Retrieval

Tourist image retrieval has attracted increasing attention from researchers. Mainly, supervised deep hash methods have significantly boosted the retrieval performance, which takes hand-crafted features as inputs and maps the high-dimensional binary feature vector to reduce feature-searching complexity. However, their performance depends on the supervised labels, but few labeled temporal and discriminative information is available in tourist images. This paper proposes an improved deep hash to learn enhanced hash codes for tourist image retrieval. It jointly determines image representations and hash functions with deep neural networks and simultaneously enhances the discriminative capability of tourist image hash codes with refined semantics of the accompanying relationship. Furthermore, we have tuned the CNN to implement end-to-end training hash mapping, calculating the semantic distance between two samples of the obtained binary codes. Experiments on various datasets demonstrate the superiority of the proposed approach compared to state-of-the-art shallow and deep hashing techniques.

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Discriminative Deep Hashing for Scalable Face Image Retrieval

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2017/315 ◽

2017 ◽

Cited By ~ 14

Author(s):

Jie Lin ◽

Zechao Li ◽

Jinhui Tang

Keyword(s):

Image Retrieval ◽

Large Scale ◽

State Of The Art ◽

Face Image ◽

Superior Performance ◽

Prediction Errors ◽

Unified Framework ◽

Multi Scale ◽

Deep Hashing ◽

Hash Codes

With the explosive growth of images containing faces, scalable face image retrieval has attracted increasing attention. Due to the amazing effectiveness, deep hashing has become a popular hashing method recently. In this work, we propose a new Discriminative Deep Hashing (DDH) network to learn discriminative and compact hash codes for large-scale face image retrieval. The proposed network incorporates the end-to-end learning, the divide-and-encode module and the desired discrete code learning into a unified framework. Specifically, a network with a stack of convolution-pooling layers is proposed to extract multi-scale and robust features by merging the outputs of the third max pooling layer and the fourth convolutional layer. To reduce the redundancy among hash codes and the network parameters simultaneously, a divide-and-encode module to generate compact hash codes. Moreover, a loss function is introduced to minimize the prediction errors of the learned hash codes, which can lead to discriminative hash codes. Extensive experiments on two datasets demonstrate that the proposed method achieves superior performance compared with some state-of-the-art hashing methods.

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Locality Constrained Deep Supervised Hashing for Image Retrieval

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2017/499 ◽

2017 ◽

Cited By ~ 1

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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Deep Binary Representation for Efficient Image Retrieval

Advances in Multimedia ◽

10.1155/2017/8961091 ◽

2017 ◽

Vol 2017 ◽

pp. 1-10 ◽

Cited By ~ 5

Author(s):

Xuchao Lu ◽

Li Song ◽

Rong Xie ◽

Xiaokang Yang ◽

Wenjun Zhang

Keyword(s):

Image Retrieval ◽

Hamming Distance ◽

Binary Representation ◽

Deep Network ◽

Deep Hashing ◽

Hash Code ◽

Representation Method ◽

The Relationship ◽

Determining Factor ◽

Hash Codes

With the fast growing number of images uploaded every day, efficient content-based image retrieval becomes important. Hashing method, which means representing images in binary codes and using Hamming distance to judge similarity, is widely accepted for its advantage in storage and searching speed. A good binary representation method for images is the determining factor of image retrieval. In this paper, we propose a new deep hashing method for efficient image retrieval. We propose an algorithm to calculate the target hash code which indicates the relationship between images of different contents. Then the target hash code is fed to the deep network for training. Two variants of deep network, DBR and DBR-v3, are proposed for different size and scale of image database. After training, our deep network can produce hash codes with large Hamming distance for images of different contents. Experiments on standard image retrieval benchmarks show that our method outperforms other state-of-the-art methods including unsupervised, supervised, and deep hashing methods.

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