Image Recognition using Supervised Discrete Hashing
Gowtham Sethupathi M1, Tushar Randive2, Shubham Banerjee3, Abdul Kadir4, Vishal Sah5
1Mr. Gowtham Sethupathi M, Assistant, Professor, Department of CSE, SRM Institute of Science and Technology, Ramapuram, Chennai (Tamil Nadu), India.
2Tushar Randive, Student, Department of CSE, SRM Institute of Science and Technology, Ramapuram, Chennai (Tamil Nadu), India.
3Shubham Banerjee, Student, Department of CSE, SRM Institute of Science and Technology, Ramapuram, Chennai (Tamil Nadu), India.
4Abdul Kadir, Student, Department of CSE, SRM Institute of Science and Technology, Ramapuram, Chennai (Tamil Nadu), India.
5Vishal Kumar Sah, Student, Department of CSE, SRM Institute of Science and Technology, Ramapuram, Chennai (Tamil Nadu), India.
Manuscript received on 07 April 2019 | Revised Manuscript received on 20 April 2019 | Manuscript published on 30 April 2019 | PP: 1560-1565 | Volume-8 Issue-6, April 2019 | Retrieval Number: F4069048619/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Information subsidiary hashing has as of late pulled in concern due to having the capacity to help helpful revival and capacity of high-dimensional data. Here, we are going to derive one new hashing method which is also called as “Supervised Discrete Hashing” to give the best of effective search result. It consists of one mandatory database which is a collection of different classes of images, and keys to that images to search. It utilizes smallest quantity squares replace matrix and normal pattern encoding in the form of 0-1. After getting the lattice information of each and every image in the byte form it will compare that with the input byte code to search for particular input image with the dataset and after comparisons it will give the result that whether the image is there in the database or not. As our defined method is mainly focuses on data or the statistics in the dataset, it is one of the type of information dependent relative hashing method. For further clarifications we are using two databases to collect the images from them and then use this method to prove if image is there in it or not there. An experimental outcomes depicts the effectiveness and more efficient way to hash the data.
Keyword: Information Dependent Relative Hashing, Smallest Quantity Squares, Supervised Discrete Hashing (SDH), and Byte Form.
Scope of the Article: Image analysis and Processing