Image retrieval from the web using multiple features - Vadivel, A.; Sural, Shamik; Majumdar, A.K. (Online information review, 2009)
Purpose - The main obstacle in realising semantic-based image retrieval from the web is that it is difficult to capture semantic description of an image in low-level features. Text-based keywords can be generated from web documents to capture semantic information for narrowing down the search space. The combination of keywords and various low-level features effectively increases the retrieval precision. The purpose of this paper is to propose a dynamic approach for integrating keywords and low-level features to take advantage of their complementary strengths.Design methodology approach - Image semantics are described using both low-level features and keywords. The keywords are constructed from the text located in the vicinity of images embedded in HTML documents. Various low-level features such as colour histograms, texture and composite colour-texture features are extracted for supplementing keywords.Findings - The retrieval performance is better than that of various recently proposed techniques. The experimental results show that the integrated approach has better retrieval performance than both the text-based and the content-based techniques.Research limitations implications - The features of images used for capturing the semantics may not always describe the content.Practical implications - The indexing mechanism for dynamically growing features is challenging while practically implementing the system.Originality value - A survey of image retrieval systems for searching images available on the internet found that no internet search engine can handle both low-level features and keywords as queries for retrieving images from WWW so this is the first of its kind.