A comparative study of hash based approximate nearest neighbor learning and its application in image retrieval
Plenty of data are available due to the growth of digital technology that creates a high expectation in retrieving the relevant images, accurately and efficiently for a given query image. For searching the relevant images efficiently for the Large Scale dataset, the searching algorithm should have f...
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| Published in | The Artificial intelligence review Vol. 52; no. 1; pp. 323 - 355 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Dordrecht
Springer Netherlands
01.06.2019
Springer Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0269-2821 1573-7462 |
| DOI | 10.1007/s10462-017-9591-1 |
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| Abstract | Plenty of data are available due to the growth of digital technology that creates a high expectation in retrieving the relevant images, accurately and efficiently for a given query image. For searching the relevant images efficiently for the Large Scale dataset, the searching algorithm should have fast access capability. The existing Exact Nearest Neighbor search performs in linear time and so it takes more time as both the dataset and data dimension increases. As a remedy to provide sub-linear/logarithmic time complexity, usage of Approximate Nearest Neighbor (ANN) algorithms is emerging at a rapid rate. This paper discusses about the importance of ANN and their general classification; the different categories involved in Learning to Hash has been analyzed with their pros and cons; different bit assignment types and methods to minimize the Quantization Errors have been reviewed along with its merits and demerits. Therefore, it serves to increase the efficiency of the Image Retrieval process in Large Scale. |
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| AbstractList | Plenty of data are available due to the growth of digital technology that creates a high expectation in retrieving the relevant images, accurately and efficiently for a given query image. For searching the relevant images efficiently for the Large Scale dataset, the searching algorithm should have fast access capability. The existing Exact Nearest Neighbor search performs in linear time and so it takes more time as both the dataset and data dimension increases. As a remedy to provide sub-linear/logarithmic time complexity, usage of Approximate Nearest Neighbor (ANN) algorithms is emerging at a rapid rate. This paper discusses about the importance of ANN and their general classification; the different categories involved in Learning to Hash has been analyzed with their pros and cons; different bit assignment types and methods to minimize the Quantization Errors have been reviewed along with its merits and demerits. Therefore, it serves to increase the efficiency of the Image Retrieval process in Large Scale. |
| Audience | Academic |
| Author | Arulmozhi, P. Abirami, S. |
| Author_xml | – sequence: 1 givenname: P. surname: Arulmozhi fullname: Arulmozhi, P. email: arulmozhikec@gmail.com organization: Department of Information Science and Technology, College of Engineering, Anna University – sequence: 2 givenname: S. surname: Abirami fullname: Abirami, S. organization: Department of Information Science and Technology, College of Engineering, Anna University |
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| CitedBy_id | crossref_primary_10_1007_s13369_024_09627_w crossref_primary_10_1007_s11042_020_10499_z crossref_primary_10_1007_s10462_022_10334_x crossref_primary_10_1007_s00371_020_01993_4 |
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| Keywords | Approximate nearest neighbor Learning to hash Deep hashing Hash based ANN |
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| SubjectTerms | Algorithms Artificial Intelligence Artificial neural networks Classification Comparative studies Computer Science Datasets Digital imaging Equipment and supplies Hash based algorithms Image management Image processing Image retrieval Learning Retrieval Search algorithms Searching |
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| Title | A comparative study of hash based approximate nearest neighbor learning and its application in image retrieval |
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