The rapid development of commercial image search engines allows users to easily retrieve a large number of images, simply by typing in a text query usually on a search engine. However, a problem with the existing search engines is the search results only use a relevance of surrounding text information of images associated with the text query. The existing search engines do not use image visual information in providing search results. As a result, there are ongoing efforts to improve image search results by leveraging the image content, which includes useful image visual information.
The growth of digital image content has made it more of a challenge to browse through the large amount of search results. To help with retrieving images, techniques have been tried for image search results refinement. Two conventional techniques commonly employed to assist with results refinement are content based reranking and IntentSearch.
Content based reranking may rely on image clustering and categorization to provide a high-level description of a set of images. While content based reranking uses visual information to reorder the search results, it does not take into consideration the intent of the user. On the other hand, IntentSearch provides an interface to allow users to indicate a few images of interests, and automatically attempts to guess the intent of the user to reorder image search results. However, guessing the intent of the user is somewhat difficult based on selected images. Thus, these conventional approaches do not really address the intent of the user for the images along with using image visual information.
Therefore, it is desirable to find ways to refine image search results through user interactions with the image visual information.