Name
Abstract | ||
|---|---|---|
Recent years, the efficiency and accuracy of content based on image retrieval (CBIR) are two research directions. In order to improve retrieval precision, firstly, we use a fusion framework, combined with Color Difference Histogram (CDH) and Micro-structure Descriptor (MSD) which are two kinds of image descriptors to calculate distances between all images and transform the distances between images into similarities. Then we utilize the similarities to construct a similarity matrix. Secondly, by using the similarity matrix, we construct a hypergraph, the hypergraph combined with relevance feedback technique is exploited to rank images and get similar images. Extensive experiments are carried out on the Corel-5K dataset and get a very good performance. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/CISP-BMEI.2017.8301985 | 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) |
Keywords | Field | DocType |
image retrieval,hypergraph,fusion framework | Computer vision,Histogram,Relevance feedback,Color image retrieval,Pattern recognition,Computer science,Hypergraph,Fusion,Image retrieval,Feature extraction,Artificial intelligence,Color difference | Conference |
ISBN | Citations | PageRank |
978-1-5386-1938-4 | 0 | 0.34 |
References | Authors | |
27 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Suliang Yu | 1 | 0 | 1.01 |
| Dongmei Niu | 2 | 2 | 6.44 |
| Xiuyang Zhao | 3 | 73 | 13.60 |
| Mingjun Liu | 4 | 0 | 2.70 |