To represent each set images from multiple set effectively exploited for representation May probabilistic distribution method was use to compare the sets and makes the system slow for comparison. But for large set it is very difficult to compromise with system and hence compact representation of each set must require for retrieving images. Learning-based hashing is the method which was used on large scale for retrieving of data. Whereas many hashing method encodes the image individually for representation of same object or the user. During the research resolving of the hash method by making use of some network model parameters helps to retrieve the image in less processing time and with greater accuracy. During the query of the image searc...
Recently, deep learning to hash has extensively been applied to image retrieval, due to its low stor...
Similarity-based image hashing represents crucial technique for visual data storage reduction and ex...
In this thesis we explore methods which learn compact hash coding schemes to encode image databases ...
Hashing is a popular approximate nearest neighbor search approach for large-scale image retrieval. S...
With the fast growing number of images uploaded every day, efficient content-based image retrieval b...
Efficient methods that enable high and rapid image retrieval are continuously needed, especially wit...
Deep hashing methods utilize an end-to-end framework to mutually learn feature representations and h...
Approximate nearest neighbor search is an efficient strategy for large-scale image retrieval. Encour...
As more and more image data are stored in the encrypted form in the cloud computing environment, it ...
Due to the storage and retrieval efficiency, hashing has been widely deployed to approximate nearest...
[[abstract]]This paper proposes a Content-Based Images Retrieval (CBIR) system which uses a modified...
This paper presents a new hashing method to learn the compact binary codes for implementing a large-...
Similarity-preserving hashing is a widely-used method for nearest neighbour search in large-scale im...
Content-based histopathology image retrieval can be a useful technique for help in diagnosing variou...
This thesis is devoted to the analysis and implementation of image hashing based on the article "Rob...
Recently, deep learning to hash has extensively been applied to image retrieval, due to its low stor...
Similarity-based image hashing represents crucial technique for visual data storage reduction and ex...
In this thesis we explore methods which learn compact hash coding schemes to encode image databases ...
Hashing is a popular approximate nearest neighbor search approach for large-scale image retrieval. S...
With the fast growing number of images uploaded every day, efficient content-based image retrieval b...
Efficient methods that enable high and rapid image retrieval are continuously needed, especially wit...
Deep hashing methods utilize an end-to-end framework to mutually learn feature representations and h...
Approximate nearest neighbor search is an efficient strategy for large-scale image retrieval. Encour...
As more and more image data are stored in the encrypted form in the cloud computing environment, it ...
Due to the storage and retrieval efficiency, hashing has been widely deployed to approximate nearest...
[[abstract]]This paper proposes a Content-Based Images Retrieval (CBIR) system which uses a modified...
This paper presents a new hashing method to learn the compact binary codes for implementing a large-...
Similarity-preserving hashing is a widely-used method for nearest neighbour search in large-scale im...
Content-based histopathology image retrieval can be a useful technique for help in diagnosing variou...
This thesis is devoted to the analysis and implementation of image hashing based on the article "Rob...
Recently, deep learning to hash has extensively been applied to image retrieval, due to its low stor...
Similarity-based image hashing represents crucial technique for visual data storage reduction and ex...
In this thesis we explore methods which learn compact hash coding schemes to encode image databases ...