Algorithms for clustering data
Algorithms for clustering data
WordNet: a lexical database for English
Communications of the ACM
Does organisation by similarity assist image browsing?
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Time as essence for photo browsing through personal digital libraries
Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries
Integrated Browsing and Querying for Image Databases
IEEE MultiMedia
MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
FeedbackBypass: A New Approach to Interactive Similarity Query Processing
Proceedings of the 27th International Conference on Very Large Data Bases
Evaluating Refined Queries in Top-k Retrieval Systems
IEEE Transactions on Knowledge and Data Engineering
The Electronic Road: Personalized Content Browsing
IEEE MultiMedia
| Hi-index | 0.00 |
In this paper we describe PIBE, a new Personalizable Image Browsing Engine that allows an effective visual exploration of large image collections combining computer vision and database techniques. The principal features of PIBE include the possibility of modifying the browsing structure by means of graphical personalization actions provided by the visual interface, and of persistently storing such customizations for subsequent browsing sections. The PIBE hierarchical browsing structure, called Browsing Tree, can be locally customized, thus avoiding global reorganizations, which are clearly unfeasible with large collections. Indeed, each node of the Browsing Tree has associated a cluster of images and a specific dissimilarity function. We present the basic principles of the PIBE engine, and report some experimental results showing the effectiveness and the efficiency of the browsing and personalization functionalities provided.