An explorative study of interface support for image searching

In this paper we study interfaces for image retrieval systems. Current image retrieval interfaces are limited to providing query facilities and result presentation. The user can inspect the results and possibly provide feedback on their relevance for the current query. Our approach, in contrast, encourages the user to group and organise their search results and thus provide more fine-grained feedback for the system. It combines the search and management process, which - according to our hypothesis - helps the user to onceptualise their search tasks and to overcome the query formulation problem. An evaluation, involving young design-professionals and di®erent types of information seeking scenarios, shows that the proposed approach succeeds in encouraging the user to conceptualise their tasks and that it leads to increased user satisfaction. However, it could not be shown to increase performance. We identify the problems in the current setup, which when eliminated should lead to more effective searching overall

Maximum margin object tracking with weighted circulant feature maps

Support vector machine (SVM) based tracking algorithms training with dense circulant samples have shown favourable performance due to its strong discriminative power and high efficiency. However, the challenges caused by the circulant sampling remain unaddressed. In this study, the authors give each training sample a weight based on their accuracy to reduce the influence of inaccurate samples. Moreover, they reform the SVM model with weighted circulant training samples. Secondly, they advocate an efficient solution by using the property of circulant matrices to solve the learning problem. Thirdly, a model update strategy is introduced to prevent the tracking models polluted by wrong samples. Experimental results on large benchmark datasets with 50 and 100 video sequences demonstrate that the authors’ tracking algorithms achieve state‐of‐art performance in terms of precision and accuracy. In addition, their tracker runs in real time

IntelliSearch: Intelligent Search for Images and Text on the Web

Abstract. IntelliSearch is a complete and fully automated information retrieval system for the Web. It supports fast and accurate responses to queries addressing text and images in Web pages by incorporating stateof-the-art text and Web link information indexing and rertieval methods in conjunction with efficient ranking of Web pages and images by importance (authority). Searching by semantic similarity for discovering information related to user’s requests (but not explicitly specified in the queries) is a distinguishing feature of the system. IntelliSearch stores a crawl of the Web with more than 1,5 million Web pages with images and is accessible on the Internet 3. It offers an ideal test-bed for experimentation and training and serves as a framework for a realistic evaluation of many Web image retrieval methods.