Logo Recognition Using Textual and Visual Search

The amount of digital data transmitting via internet has reached an enormous level. In order to conduct efficient web data analysis, effective web mining tools are needed. Logos, which represent companies’ brands, are highly regarded in a business world. These logos embedded in ordinary pictures could give an indication of popularity of the companies and their products in a region. Therefore, it is imperative to build a computer system to extract company logos from these pictures. In this paper, a Logo on Map (LoM) system is proposed, which consists of three modules: picture extraction module (PEM), logo matching module (LMM) and web mapping module (WMM). Only the first two modules are covered in this paper. The PEM is based on a keyword textual search while the LMM is a visual search using SIFT (ScaleInvariant Feature Transform) algorithm. The three experiments are conducted using different sets of pictures extracted from the Flickr website. The experimental results have proven that visual search is more accurate than textual search and also demonstrated that LoM could be used to discover hidden knowledge beyond logos.