Deep Binary Representation for Efficient Image Retrieval
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...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-01-01
|
| Series: | Advances in Multimedia |
| Online Access: | http://dx.doi.org/10.1155/2017/8961091 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832564782974107648 |
|---|---|
| author | Xuchao Lu Li Song Rong Xie Xiaokang Yang Wenjun Zhang |
| author_facet | Xuchao Lu Li Song Rong Xie Xiaokang Yang Wenjun Zhang |
| author_sort | Xuchao Lu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d9746245ccbb4fcdbe8cce5d0fd9ff81 |
| institution | Kabale University |
| issn | 1687-5680 1687-5699 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Multimedia |
| spelling | doaj-art-d9746245ccbb4fcdbe8cce5d0fd9ff812025-02-03T01:10:17ZengWileyAdvances in Multimedia1687-56801687-56992017-01-01201710.1155/2017/89610918961091Deep Binary Representation for Efficient Image RetrievalXuchao Lu0Li Song1Rong Xie2Xiaokang Yang3Wenjun Zhang4Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University, Shanghai, ChinaInstitute of Image Communication and Network Engineering, Shanghai Jiao Tong University, Shanghai, ChinaInstitute of Image Communication and Network Engineering, Shanghai Jiao Tong University, Shanghai, ChinaInstitute of Image Communication and Network Engineering, Shanghai Jiao Tong University, Shanghai, ChinaInstitute of Image Communication and Network Engineering, Shanghai Jiao Tong University, Shanghai, ChinaWith 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.http://dx.doi.org/10.1155/2017/8961091 |
| spellingShingle | Xuchao Lu Li Song Rong Xie Xiaokang Yang Wenjun Zhang Deep Binary Representation for Efficient Image Retrieval Advances in Multimedia |
| title | Deep Binary Representation for Efficient Image Retrieval |
| title_full | Deep Binary Representation for Efficient Image Retrieval |
| title_fullStr | Deep Binary Representation for Efficient Image Retrieval |
| title_full_unstemmed | Deep Binary Representation for Efficient Image Retrieval |
| title_short | Deep Binary Representation for Efficient Image Retrieval |
| title_sort | deep binary representation for efficient image retrieval |
| url | http://dx.doi.org/10.1155/2017/8961091 |
| work_keys_str_mv | AT xuchaolu deepbinaryrepresentationforefficientimageretrieval AT lisong deepbinaryrepresentationforefficientimageretrieval AT rongxie deepbinaryrepresentationforefficientimageretrieval AT xiaokangyang deepbinaryrepresentationforefficientimageretrieval AT wenjunzhang deepbinaryrepresentationforefficientimageretrieval |