Content based Image Retrieval using Advanced Techniques

Web data extraction has been an important part for many Web data analysis applications. This paper formulates the data extraction such as the image retrieval using advanced techniques. I propose an unsupervised, page-level data extraction approach to deduce the schema and templates for each individual Deep Website that contains either singleton or multiple data records in one Webpage. FiVaTech applies tree matching, tree alignment, and advanced techniques to achieve the challenging task. In experiments, FiVaTech has much higher precision than EXALG and is comparable with other record level extraction systems like ViPER and MSE. The experiments show an encouraging result for the test pages used in many state-of-the-art Web data extraction works. Since the term has been widely used to describe the process of retrieving desired images from a large collection on the basis of features such as image that can be automatically extracted from the data themselves. The features used for retrieval can be either primitive or semantic, but the extraction process must be pre dominantly automatic. Retrieval of images by manually-assigned keywords is definitely CBIR as the term is generally understood – even if the keywords describe image content.