Semantic Based Image Retrieval using multi-agent model by searching and filtering replicated web images

The volume of web has increased tremendously, as a result retrieving useful and relevant information in terms of textual information or visual information has become a difficult task. The current search engines still depend on textual descriptions for retrieving images from the web. It is difficult to search for the relevant images using commercial search engines. The results from commercial image search engines are often mixed up with irrelevant and redundant images extracted from the web. This paper on SBIR(Semantic Based Image Retrieval) proposes a method to find user intended images from the web using proposed image crawling mechanism. The proposed framework overcomes the two major problems in case of retrieving user centric images from the web: freshness problem and redundancy problem. The proposed framework can also be used as personalized image search engine which effectively extract the text information on the web to semantically describe the retrieved images. Experiments are designed and conducted to test the performance of proposed image crawling approach. The experimental results illustrate substantial improvement in the image crawling strategy, especially when the search strings is combined with low level image features.