Design of Collection and Semantic Annotation System for Web Images

2013 ◽  
Vol 8 (3) ◽  
Author(s):  
Ruojuan Xue ◽  
Wenpeng Lu ◽  
Jinyong Cheng
2014 ◽  
Vol 4 (1) ◽  
pp. 69
Author(s):  
Chekry Abderrahman ◽  
Oriche Aziz ◽  
Khaldi Mohamed

This paper presents a system based on intelligent agents for the semantic annotation of learning resources taking into account the context of training. Semantic annotations systems rarely treat existing semantic annotations in the field of distance education (e-learning), most researchers in the field of education limits annotations to specific cases (teacher annotation, learner annotation, annotation of electronic documents etc.) these annotations are edited by users with an annotation tools, by cons in our approach, we propose a semantic annotation system based on intelligent agents that manage semantic annotations of educational resources, these annotations are guided by domain ontologies and ontology applications. We believe that the original resource annotations, a storehouse of learning objects standardized by LOM profile, these learning objects are managed using an ontology learning.


2010 ◽  
Vol 439-440 ◽  
pp. 1361-1366 ◽  
Author(s):  
Ruo Juan Xue

In order to effectively utilize Web images to construct instructional resource database, a novel approach is proposed in this paper. With this approach, Web images and their semantics can be automatically downloaded, extracted and stored in resource database and the semantics can be refined by user feedback in retrieval progress. Image topic dictionary is built as the basis to extract semantics. Eight kinds of text are extracted as semantic source from Web pages. Based on image topic dictionary, image semantics can be extracted from the eight kinds of text. In order to further improve the accuracy of semantic extraction, we propose relevance feedback mechanism. Users can provide feedback to refine semantic annotation. The experimental results show that the approach is effective, in which high construction efficiency and quality can be achieved. The approach is better than manual annotation in efficiency and better than automatic annotation in accuracy. The similar methods can be applied to construct resource database of other forms of multimedia.


2021 ◽  
pp. 368-372
Author(s):  
Jimena Andrade-Hoz ◽  
Guillermo Vega-Gorgojo ◽  
Irene Ruano ◽  
Miguel L. Bote-Lorenzo ◽  
Juan I. Asensio-Pérez ◽  
...  

2013 ◽  
Vol 373-375 ◽  
pp. 624-628
Author(s):  
Jing Jing Zhang ◽  
Yan Cao ◽  
Xiang Wei Mu

The image retrieval based on emotional keywords is required by users. But now it is short of ways to mark emotional semantic for images, especially the automatic methods, narrow and inaccurate. In this paper, we use the web images which are marked relatively comprehensively and accurately as training samples to generate emotional semantic.


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