Concept-based indexing of annotated images using semantic DNA

  • Authors:

  • Syed Abdullah Fadzli;Rossitza Setchi

  • Affiliations:

  • Cardiff University, School of Engineering, The Parade, Cardiff CF24 3AA, UK and Faculty of Informatics, University of Sultan Zainal Abidin, Gong Badak Campus, 21300 Kuala Terengganu, Terengganu, M ...;Cardiff University, School of Engineering, The Parade, Cardiff CF24 3AA, UK

  • Venue:
  • Engineering Applications of Artificial Intelligence
  • Year:
  • 2012

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Abstract

One of the challenges in image retrieval is dealing with concepts which have no visual appearance in the images or are not used as keywords in their annotations. To address this problem, this paper proposes an unsupervised concept-based image indexing technique which uses a lexical ontology to extract semantic signatures called 'semantic chromosomes' from image annotations. A semantic chromosome is an information structure, which carries the semantic information of an image; it is the semantic signature of an image in a collection expressed through a set of semantic DNA (SDNA), each of them representing a concept. Central to the concept-based indexing technique discussed is the concept disambiguation algorithm developed, which identifies the most relevant 'semantic DNA' (SDNA) by measuring the semantic importance of each word/phrase in the annotation. The concept disambiguation algorithm is evaluated using crowdsourcing. The experiments show that the algorithm has better accuracy (79.4%) than the accuracy demonstrated by other unsupervised algorithms (73%) in the 2007 Semeval competition. It is also comparable with the accuracy achieved in the same competition by the supervised algorithms (82-83%) which contrary to the approach proposed in this paper have to be trained with large corpora. The approach is currently applied to the automated generation of mood boards used as an inspirational tool in concept design.