An intelligent annotation-based image retrieval system based on RDF descriptions

The notions of concept and instance are proposed to express the semantics of images.An image annotation model is proposed to annotate images at three levels.An intelligent ABIR system is implemented based on RDF descriptions.The problems of synonyms and homonyms are addressed in the our ABIR system.The proposed ABIR system provides a way to search with calculation. Display Omitted In this paper, we aim at improving text-based image search using Semantic Web technologies. We introduce our notions of concept and instance in order to better express the semantics of images, and present an intelligent annotation-based image retrieval system. We test our approach on the Flickr8k dataset. From the provided captions, we generate annotations at three levels (sentence, concept and instance). These annotations are stored as RDF triples and can be queried to find images. The experimental results show that using concepts and instances to annotate images flexibly can improve the intelligence of the image retrieval system: (1) with annotations at concept level, it enables to create semantic links between concepts and then addresses many challenges, such as the problems of synonyms and homonyms; (2) with annotations at instance level, it can count things (e.g., two people, three animals) or identify a same concept.

[1]  Xing Xu,et al.  Learning multi-task local metrics for image annotation , 2014, Multimedia Tools and Applications.

[2]  George A. Miller,et al.  WordNet: A Lexical Database for English , 1995, HLT.

[3]  A. T. Schreiber,et al.  Semantic Annotation of Image Collections , 2003 .

[4]  T. Dharani,et al.  A survey on content based image retrieval , 2013, 2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering.

[5]  Qingming Huang,et al.  Robust Spatial Consistency Graph Model for Partial Duplicate Image Retrieval , 2013, IEEE Transactions on Multimedia.

[6]  Carlos Viegas Damásio,et al.  Improving tag-based image search by using linked open data , 2013, OAIR.

[7]  Jens Lehmann,et al.  DBpedia: A Nucleus for a Web of Open Data , 2007, ISWC/ASWC.

[8]  Tim Chown,et al.  Get All, Filter Details - On the Use of Regular Expressions in SPARQL Queries , 2014 .

[9]  Geun-Duk Park,et al.  Linked tag: image annotation using semantic relationships between image tags , 2014, Multimedia Tools and Applications.

[10]  Michael Grobe,et al.  RDF, Jena, SparQL and the 'Semantic Web' , 2009, SIGUCCS '09.

[11]  Xing Xu,et al.  Image annotation with incomplete labelling by modelling image specific structured loss , 2015 .

[12]  Pierre Andrews,et al.  Semantic Annotation of Images on Flickr , 2011, ESWC.

[13]  Peter Young,et al.  Framing Image Description as a Ranking Task: Data, Models and Evaluation Metrics , 2013, J. Artif. Intell. Res..

[14]  Xiaoming Zhang,et al.  Improving image tags by exploiting web search results , 2011, Multimedia Tools and Applications.

[15]  Xing Xu Non-member,et al.  Image annotation with incomplete labelling by modelling image specific structured loss , 2015 .

[16]  Mohammed A. Balubaid,et al.  Semantic Image Retrieval: An Ontology Based Approach , 2015 .

[17]  Orri Erling,et al.  RDF Support in the Virtuoso DBMS , 2007, CSSW.

[18]  N. Magesh,et al.  Semantic Image Retrieval Based on Ontology and SPARQL Query , 2011 .