Recent Improvements in Image Retrieval

2014 ◽  
Vol 989-994 ◽  
pp. 4069-4073
Author(s):  
Yan Sheng Zeng ◽  
Xu Lin Ying

The target of this paper is to introduce the improvement of the technique of image retrieval. At first, it comes up with the concept of image retrieval and shows the importance of this technique. As the techniques of multimedia and Internet are developing rapidly, the resources of images that users obtain are also extended. And then this paper gives the problem about the image retrieval, namely the information of images are disordering. As the result, it is significant to do the effective organization, management and retrieval based on the increasingly extensive image information storage. After that, this paper presents the concept of TBIR and CBIR and gives the definitions of them. It proposes an issue that CBIR is the improvement of TBIR. Based on CBIR, there are also some disadvantages that need to be improved. In terms of the main point of CBIR, the paper raises that the annotation is one of the most difficult techniques that need to be promoted. Then it describes some algorithms about the technique of automatic image annotation. After these algorithms, the paper shows the challenges and developing direction of the technique of image retrieval. At last, it presented the conclusion to emphasize the main points of this paper.

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Hongwei Ge ◽  
Zehang Yan ◽  
Jing Dou ◽  
Zhen Wang ◽  
ZhiQiang Wang

Automatic image annotation is for more accurate image retrieval and classification by assigning labels to images. This paper proposes a semisupervised framework based on graph embedding and multiview nonnegative matrix factorization (GENMF) for automatic image annotation with multilabel images. First, we construct a graph embedding term in the multiview NMF based on the association diagrams between labels for semantic constraints. Then, the multiview features are fused and dimensions are reduced based on multiview NMF algorithm. Finally, image annotation is achieved by using the new features through a KNN-based approach. Experiments validate that the proposed algorithm has achieved competitive performance in terms of accuracy and efficiency.


2013 ◽  
Vol 109 ◽  
pp. 33-48 ◽  
Author(s):  
Dumitru Dan Burdescu ◽  
Cristian Gabriel Mihai ◽  
Liana Stanescu ◽  
Marius Brezovan

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