Automatic Website summarization by image content: A case study with logo and trademark images
Image-based abstraction (or summarization) of a Website is the process of extracting the most characteristic (or important) images from it. The criteria for measuring the importance of images on Websites are based on their frequency of occurrence, characteristics of their content, and Web link information. As a case study, this work focuses on logo and trademark images. These are important characteristic signs of corporate Websites or of products presented there. The proposed method incorporates machine learning for distinguishing logo and trademarks from images of other categories (e.g., landscapes and faces). Because the same logo or trademark may appear many times in various forms within the same Website, duplicates are detected and only unique logo and trademark images are extracted. These images are then ranked by importance taking frequency of occurrence, image content, and Web link information into account. The most important logos and trademarks are finally selected to form the image-based summary of a Website. Evaluation results of the method on real Websites are also presented. The method has been implemented and integrated into a fully automated image-based summarization system, which is accessible on the Web at http://www.intelligence.tuc.gr/websummarization.
website summarization logo trademark machine learning feature extraction