HII: Histogram Inverted Index for Fast Images Retrieval
| Main Authors: | Munarko, Yuda, Minarno, Agus Eko |
|---|---|
| Format: | Article PeerReviewed Book |
| Bahasa: | eng |
| Terbitan: |
International Journal of Electrical and Computer Engineering (IJECE)
, 2018
|
| Subjects: | |
| Online Access: |
http://eprints.umm.ac.id/60744/18/Peer%20Review%20-%20Minarno%20-%20CBIR%20Histogram%20Indexing%20Inverted%20Index.pdf http://eprints.umm.ac.id/60744/19/Similarity%20-%20Munarko%20Minarno%20-%20CBIR%20Histogram%20Indexing%20Inverted%20Index.pdf http://eprints.umm.ac.id/60744/20/Munarko%20Minarno%20-%20CBIR%20Histogram%20Indexing%20Inverted%20Index.pdf http://eprints.umm.ac.id/60744/ http://ijece.iaescore.com/index.php/IJECE/article/view/8037 |
Daftar Isi:
- This work aims to improve the speed of search by creating an indexing structure in Content Based Images Retrieval (CBIR) system. We utilised an inverted index structure that usually used in text retrieval with a modification. The modified inverted index is built based on histogram data that generated using Multi Texton Histogram (MTH) and Multi Texton CoOccurrence Descriptor (MTCD) from 10,000 images of Corel dataset. When building the inverted index, we normalised value of each feature into a real number and considered pairs of feature and value that owned by a particular number of images. Based on our investigation, on MTCD histogram of 5,000 data test, we found that by considering histogram variable values which owned by maximum 12% of images, the number of comparison for each query can be reduced by 67.47% in a rate, the precision is 82.2%, and the rate of access to disk is 32.83%. Furthermore, we named our approach as Histogram Inverted Index (HII).