Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Does organisation by similarity assist image browsing?
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Integrated Browsing and Searching of Large Image Collections
VISUAL '00 Proceedings of the 4th International Conference on Advances in Visual Information Systems
MediaMill: exploring news video archives based on learned semantics
Proceedings of the 13th annual ACM international conference on Multimedia
Information Visualization: Beyond the Horizon
Information Visualization: Beyond the Horizon
Interactive access to large image collections using similarity-based visualization
Journal of Visual Languages and Computing
A next generation browsing environment for large image repositories
Multimedia Tools and Applications
Interacting with image collections: visualisation and browsing of image repositories
Proceedings of the 20th ACM international conference on Multimedia
Interactive browsing of image repositories
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
Interactive exploration of image collections on mobile devices
AMT'12 Proceedings of the 8th international conference on Active Media Technology
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Category search can be supported by methods that allow intelligent selection of potentially relevant images. This paper explores the use of a nearest neighbor network in the selection process. We created a prototype that visualizes the network of images. As in the nearest neighbor network the images are connected to similar images we assume that if an image is selected or deselected, the same action can be performed on its neighbors. This results in five possible actions: selecting an image, selecting an image with its nearest neighbors, deselecting an image, deselecting an image with its nearest neighbors and growing the selection with all the nearest neighbors. Using these actions four different interaction scenarios are defined which are evaluated using experiments. Our experiments show that the nearest neighbor network can have a positive effect on the interaction effort needed to select images, compared to the baseline of sequentially selecting images. The experiments further show that the different interaction scenarios can cater for different values of allowed interaction effort with respect to the requirements on precision and recall.