Name
Abstract | ||
|---|---|---|
Facing the explosive growth of personal photos, an effective classification tool is becoming an urgent need for users to categorize images efficiently with personal preferences. As previous researches mainly focus on the accuracy of automatic classification within the pre-defined label space, they cannot be used directly for the personalized categorization. In this paper, we propose a data-driven classification method for personalized image classification tasks which can categorize images group by group. Firstly, we describe images from both the view of appearance and the view of semantic. Then, an iterative framework which incorporates spectral clustering with user intervention is utilized to categorize images group by group. To improve the quality of clustering, we propose an online multi-view metric learning algorithm to learn the similarity metrics in accordance with user's criterion, and constraint propagation is integrated to adjust the similarity matrix. In addition, we build a system named PicMarker based on the proposed method. Experimental results demonstrate the effectiveness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-54190-7_11 | COMPUTER VISION - ACCV 2016, PT IV |
Field | DocType | Volume |
Categorization,Spectral clustering,Fuzzy clustering,Pattern recognition,Correlation clustering,Computer science,Segmentation-based object categorization,Artificial intelligence,Conceptual clustering,Contextual image classification,Cluster analysis,Machine learning | Conference | 10114 |
ISSN | Citations | PageRank |
0302-9743 | 1 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiagao Hu | 1 | 4 | 2.07 |
| zhengxing sun | 2 | 252 | 45.27 |
| Bo Li | 3 | 8 | 8.65 |
| Shuang Wang | 4 | 46 | 10.94 |