Name
Abstract | ||
|---|---|---|
In traditional image retrieval techniques, the query results are severely affected when the images of varying illumination and scale, as well as occlusion and corrosion. Seeking to solve this problem, this paper proposed a novel multi-feature fusion and sparse coding based framework for image retrieval. In the framework, firstly, inherent features of an image are extracted, and then dictionary learning method is utilized to construct them to be dictionary features. Finally, the proposed framework introduces sparse representation model to measure the similarity between two images. The merit is that a feature descriptor is coded as a sparse linear combination with respect to dictionary feature so as to achieve efficient feature representation and robust similarity measure. In order to check the validity of the framework, this paper conducted two groups of experiments on Corel-1000 image dataset and the Stirmark benchmark based database respectively. Experimental results show that the proposed framework is much more effective than the state-of-the-art methods not only in traditional image dataset but also in varying image dataset. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/SMC.2014.6974284 | SMC |
Keywords | Field | DocType |
sparse coding-based framework,feature descriptor,feature representation,dictionary learning method,image representation,image coding,image fusion,learning (artificial intelligence),sparse representation model,dictionary learning,robust similarity measure,similarity assessment,stirmark benchmark based database,corrosion,image retrieval,sparse linear combination,corel-1000 image dataset,illumination,image retrieval techniques,multifeature fusion,multi-feature fusion,occlusion,sparse representation,dictionary features | Computer vision,Automatic image annotation,Feature detection (computer vision),Pattern recognition,K-SVD,Neural coding,Image texture,Feature (computer vision),Computer science,Image retrieval,Artificial intelligence,Visual Word | Conference |
ISSN | Citations | PageRank |
1062-922X | 2 | 0.36 |
References | Authors | |
16 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qiaosong Chen | 1 | 3 | 2.42 |
| Yuanyuan Ding | 2 | 303 | 15.04 |
| Hai Li | 3 | 2435 | 208.37 |
| Xi Wang | 4 | 4 | 1.08 |
| Jin Wang | 5 | 3 | 1.74 |
| Xin Deng | 6 | 2 | 1.37 |