Abstract :
Although relevance feedback (RF) has been extensively studied in the content-based image retrieval community, no commercial Web image search engines support RF because of scalability, efficiency, and effectiveness issues. In this paper, we propose a unified relevance feedback framework for Web image retrieval. Our framework shows advantage over traditional RF mechanisms in the following three aspects. First, during the RF process, both textual feature and visual feature are used in a sequential way. To seamlessly combine textual feature-based RF and visual feature-based RF, a query concept-dependent fusion strategy is automatically learned. Second, the textual feature-based RF mechanism employs an effective search result clustering (SRC) algorithm to obtain salient phrases, based on which we could construct an accurate and low-dimensional textual space for the resulting Web images. Thus, we could integrate RF into Web image retrieval in a practical way. Last, a new user interface (UI) is proposed to support implicit RF. On the one hand, unlike traditional RF UI which enforces users to make explicit judgment on the results, the new UI regards the users’ click-through data as implicit relevance feedback in order to release burden from the users. On the other hand, unlike traditional RF UI which hardily substitutes subsequent results for previous Ones, a recommendation scheme is used to help the users better understand the feedback process and to mitigate the possible waiting caused by RF. Experimental results on a database consisting of nearly three million Web images show that the proposed framework is wieldy, scalable, and effect
With the explosive growth of both World Wide Web and the number of digital images, there is more and more urgent need for effective Web image retrieval systems. Most of the popular commercial search engines, such as Google, Yahoo and AltaVista, support image retrieval by keywords. There are also commercial search engines dedicated to image retrieval, e.g., Pic search. A common limitation of most of the existing Web image retrieval systems is that their search process is passive, i.e., disregarding the informative interactions between users and retrieval systems. An active system should get the user into the loop so that personalized results could be provided for the specific user. To be active, the system could take advantage of relevance feedback techniques.
Relevance feedback, originally developed for information retrieval, is an online learning technique aiming at improving the effectiveness of the information retrieval system. The main idea of relevance feedback is to let the user guide the system.
During retrieval process, the user interacts with the system and rates the relevance of the retrieved documents, according to his/her subjective judgment. With this additional information, the system dynamically learns the user’s intention, and gradually presents better results. Since the introduction of relevance feedback to image retrieval in the mid-1990s, it has attracted tremendous attention in the content-based image retrieval (CBIR) community and has been shown to provide dramatic performance improvement. However, no commercial Web image search engines support relevance feedback because of usability, scalability, and efficiency issues.