|本期目录/Table of Contents|

 Li Yunyi,Miao Duoqian,Wei Zhihua*.Social tag refinement model based on feature fusion?and multi-correlation consistency[J].Journal of Nanjing University(Natural Sciences),2016,52(2):244-252.[doi:10.13232/j.cnki.jnju.2016.02.005]





Social tag refinement model based on feature fusion?and multi-correlation consistency
(1. 同济大学计算机科学与技术系,上海,201804;2. 同济大学嵌入式与服务计算教育部重点实验室,上海,201804)
Li Yunyi12 Miao Duoqian12 Wei Zhihua12*
(1.Department of Computer Science and Technology,Tongji University, Shanghai, 201804, China;
2. Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai, 201804, China)

标签精化特征融合一致性 Sylvester方程二次规划
tag refinement feature fusion consistency Sylvester function quadratic programming
In the current time, user-provided tags are accessible on photo sharing websites which facilitate further tag-based multimedia applications, such as image ranking, image retrieval and tag recommendation. However, user-supplied tags for web images are often irrelevant, imprecise and incomplete, which will lower the performances of image management tasks. And many efforts have been made to solve this problem. Image, user tags and author are three basic elements of web images. However, only one or two basic elements and few correlation consistency among them are considered in many image tag refinement algorithms. In this paper, an optimization model based on feature fusion and multi-correlation consistency is proposed for social tag refinement. Image visual features, user-supplied tags, and authors’ information are all considered in the proposed model. And multi-correlation consistency such as visual content-semantics consitency between image-pair, tag-tag correlation consistency and the user-user correlation consistency are put to use in our framework. Which will gain bettter refinement performance than those works that only consider one or two elements and few correlation consitency between web images. Traditionally, people often connect some visual features into a long vector or only choose one feature for image tag refinement task. The former will suffer from the problem of “Curse of Dimensionality” and the latter can not obtain sufficient image visual information for the task. So, a feature fusion idea is put forward in our framework. Multiple image visual features are considered and weights for each feature can be calculated automatically to estimate the importance of different features by an iteraitve process. F-score macro is used as?evaluation criterion like many other works. And comparative experiment results on MIR-Flickr dataset show that our performances are comparable with works of state-of-the-art. And the advantages of feature fusion and multi-correlation consistency are also proved by the designed experiments.


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更新日期/Last Update: 2016-03-16