An Adaptive and Asymmetric Residual Hash for Fast Image Retrieval
Hashing algorithm has attracted great attention in recent years. In order to improve the query speed and retrieval accuracy, this paper proposes an adaptive and asymmetric residual hash (AASH) algorithm based on residual hash, integrated learning, and asymmetric pairwise loss. The specific descripti...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8736226/ |
| _version_ | 1818621991605764096 |
|---|---|
| author | Shuli Cheng Liejun Wang Anyu Du |
| author_facet | Shuli Cheng Liejun Wang Anyu Du |
| author_sort | Shuli Cheng |
| collection | DOAJ |
| description | Hashing algorithm has attracted great attention in recent years. In order to improve the query speed and retrieval accuracy, this paper proposes an adaptive and asymmetric residual hash (AASH) algorithm based on residual hash, integrated learning, and asymmetric pairwise loss. The specific description of the AASH algorithm is as follows: 1) the integrated learning model is proposed based on transfer learning and multi-feature fusion strategy to learn the database hash code; 2) the residual hash model is proposed based on ResNet-50 to learn the query image hash code; 3) the asymmetric pairwise loss is proposed and the parameters of the residual hash model is optimized based on the database hash code; 4) the algorithm learns the database hash code and the query image hash code in an asymmetric manner, and integrates the feature learning part and the hash-coded part in one frame. The experimental results on three different datasets fully demonstrate that the proposed AASH method has better performance than most symmetric and asymmetric deep hash algorithms. Specifically, the optimal result of the AASH algorithm is 0.971 on Cifar10 when the hyperparameter is 100 and the hash code length is 32. The optimal result of the AASH algorithm is 0.945 on ceil images when the hyperparameter is 10 and the hash code length is 24. The optimal result of the AASH algorithm is 0.945 on FD-XJ when the hyperparameter is 15 and the hash code length is 32. In addition, the algorithm verifies convergence, time loss, and effectiveness. |
| first_indexed | 2024-12-16T18:18:04Z |
| format | Article |
| id | doaj.art-ffd8da1b1f54444dba12084721d22a61 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T18:18:04Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-ffd8da1b1f54444dba12084721d22a612022-12-21T22:21:38ZengIEEEIEEE Access2169-35362019-01-017789427895310.1109/ACCESS.2019.29227388736226An Adaptive and Asymmetric Residual Hash for Fast Image RetrievalShuli Cheng0https://orcid.org/0000-0003-0383-6797Liejun Wang1Anyu Du2College of Information Science and Engineering, Xinjiang University, Ürümqi, ChinaCollege of Information Science and Engineering, Xinjiang University, Ürümqi, ChinaCollege of Information Science and Engineering, Xinjiang University, Ürümqi, ChinaHashing algorithm has attracted great attention in recent years. In order to improve the query speed and retrieval accuracy, this paper proposes an adaptive and asymmetric residual hash (AASH) algorithm based on residual hash, integrated learning, and asymmetric pairwise loss. The specific description of the AASH algorithm is as follows: 1) the integrated learning model is proposed based on transfer learning and multi-feature fusion strategy to learn the database hash code; 2) the residual hash model is proposed based on ResNet-50 to learn the query image hash code; 3) the asymmetric pairwise loss is proposed and the parameters of the residual hash model is optimized based on the database hash code; 4) the algorithm learns the database hash code and the query image hash code in an asymmetric manner, and integrates the feature learning part and the hash-coded part in one frame. The experimental results on three different datasets fully demonstrate that the proposed AASH method has better performance than most symmetric and asymmetric deep hash algorithms. Specifically, the optimal result of the AASH algorithm is 0.971 on Cifar10 when the hyperparameter is 100 and the hash code length is 32. The optimal result of the AASH algorithm is 0.945 on ceil images when the hyperparameter is 10 and the hash code length is 24. The optimal result of the AASH algorithm is 0.945 on FD-XJ when the hyperparameter is 15 and the hash code length is 32. In addition, the algorithm verifies convergence, time loss, and effectiveness.https://ieeexplore.ieee.org/document/8736226/Residual hashasymmetric manneradaptive and asymmetric residual hash (AASH)information searchhash coding |
| spellingShingle | Shuli Cheng Liejun Wang Anyu Du An Adaptive and Asymmetric Residual Hash for Fast Image Retrieval IEEE Access Residual hash asymmetric manner adaptive and asymmetric residual hash (AASH) information search hash coding |
| title | An Adaptive and Asymmetric Residual Hash for Fast Image Retrieval |
| title_full | An Adaptive and Asymmetric Residual Hash for Fast Image Retrieval |
| title_fullStr | An Adaptive and Asymmetric Residual Hash for Fast Image Retrieval |
| title_full_unstemmed | An Adaptive and Asymmetric Residual Hash for Fast Image Retrieval |
| title_short | An Adaptive and Asymmetric Residual Hash for Fast Image Retrieval |
| title_sort | adaptive and asymmetric residual hash for fast image retrieval |
| topic | Residual hash asymmetric manner adaptive and asymmetric residual hash (AASH) information search hash coding |
| url | https://ieeexplore.ieee.org/document/8736226/ |
| work_keys_str_mv | AT shulicheng anadaptiveandasymmetricresidualhashforfastimageretrieval AT liejunwang anadaptiveandasymmetricresidualhashforfastimageretrieval AT anyudu anadaptiveandasymmetricresidualhashforfastimageretrieval AT shulicheng adaptiveandasymmetricresidualhashforfastimageretrieval AT liejunwang adaptiveandasymmetricresidualhashforfastimageretrieval AT anyudu adaptiveandasymmetricresidualhashforfastimageretrieval |