Content based image retrieval based on geo-location driven image tagging on the social web

In recently years, in the era of multimedia technologies need for information/data retrieval systems getting more attention. The data might be image, video, audio and/or text files. Digital libraries, surveillance application, web applications and many other applications that handle huge volume of data essentially have data retrieval components. These data always include large-scale of independent information with both textual and visual contents. The large numbers of images has posed increasing challenges to computer systems to store and manage data effectively and efficiently. In this paper, we proposed a method of Geo-location-based image retrieval (GLBIR). The proposed method identifies a geo location in an image using visual attention-based mechanism and represents them using its color layout descriptors and curve let descriptors. These features are extracted from geo location of query image from Flickr. The likeness between the query geo coordinates and image is ranked according to a similarity measure computed from the feature vectors. Our proposed model does not full semantic understanding of image content, uses visual metrics for example proximity, color contrast, size and nearness to image's boundaries to locate viewer's attention. We evaluate our approach on the image dataset from Flickr. We analyzed results analyzed and compared with state of art CBIR Systems and GLBIR Technique.