Image retrieval from the web using multiple features

Author(s): A. Vadivel, Shamik Sural, A.K. Majumdar

Journal: Online Information Review

Abstract:

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.

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