There is no end for google's innovation Google Image Swirl represents a concrete step towards reaching that goal. It looks at the pixel values of the top search results and organizes and presents them in visually distinctive groups. Source:Google Official Blog

In Google Image Swirl, we address this set of challenges by organizing all available information about an image set into a pairwise similarity graph, and applying novel graph-analysis algorithms to discover higher-order similarity and category information from this graph. Given the high dimensionality of image features and the noise in the data, it can be difficult to train a monolithic categorization engine that can generalize across all queries. In contrast, image similarities need only be defined for similar enough objects and trained with limited sets of data. Also, invariance to certain transformations or typical intra-class variation can be built into the perceptual similarity function. Different features or similarity functions may be selected, or learned, for different types of queries or image contents. Given a robust set of similarity functions, one can generate a graph (nodes are images and edges are similarity values) and apply graph analysis algorithms to infer similarities and categorical relationships that are not immediately obvious. In this work, we combined multiple sources of similarity such as those used in Google Similar Images, landmark recognition, Picasa's face recognition, anchor text similarity, and category-instance relationships between keywords similar to that in WordNet. It is a continuation of our prior effort [paper] to rank images based on visual similarity.