Name
Abstract | ||
|---|---|---|
This communication presents a novel system for Content Based Image Retrieval (CBIR) using Granulometry and Color Co-occurrence Features (CCF). These features are extracted directly from images using visual codebook. Relative distance measures are used to identify the similarity between the stored images and the query image. Results show that proposed method of using Granulometry and CCF is superior to most state of the art CBIR systems. The proposed system is tested on Wang image database that contains 1000 images having different categories. The performance of the system, quantified using the Average Precision Rate (APR), is very encouraging. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.4108/eai.13-4-2018.154479 | EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS |
Keywords | Field | DocType |
Granulometry Features, CCF, Content Based Image Retrieval | Granulometry,Pattern recognition,Computer science,Co-occurrence,Artificial intelligence,Content-based image retrieval,Distributed computing | Journal |
Volume | Issue | ISSN |
4 | 16 | 2032-9407 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lal Said | 1 | 0 | 0.34 |
| Khurram Khurshid | 2 | 129 | 15.94 |
| Asia Aman | 3 | 0 | 0.34 |