Erwin, Erwin (2017) Content based image retrieval for multi-objects fruits recognition using k-means and k-nearest neighbor. In: 2017 International Conference on Data and Software Engineering (ICoDSE), 1-2 Nov. 2017, Palembang.
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Abstract
The uniqueness of fruits can be observed using the colors and shapes. The fruit recognition process consists of 3 stages, namely feature extraction, clustering, and recognition. Each of stage uses different methods. The color extraction process using Fuzzy Color Histogram (FCH) method and shaping extraction using Moment Invariants (MI) method. The clustering process uses the K-Means Clustering Algorithm. The recognition process uses the k-NN method. The Content-Based Image Retrieval (CBIR) process uses image features (visual contents) to perform image searches from the database. Experimental results and analysis of fruit recognition system obtained an accuracy of 92.5% for single-object images and 90% for the multi-object image.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Subjects: | Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation. |
| Divisions: | 09-Faculty of Computer Science > 56201-Computer Systems (S1) |
| Depositing User: | Dr Erwin Erwin |
| Date Deposited: | 12 Oct 2020 00:43 |
| Last Modified: | 12 Oct 2020 00:43 |
| URI: | http://repository.unsri.ac.id/id/eprint/36526 |
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Content based image retrieval for multi-objects fruits recognition using k-means and k-nearest neighbor. (deposited 20 Apr 2020 01:05)
- Content based image retrieval for multi-objects fruits recognition using k-means and k-nearest neighbor. (deposited 12 Oct 2020 00:43) [Currently Displayed]
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