An Efficiently Computable Metric for Comparing Polygonal Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information Retrieval Experiment
Information Retrieval Experiment
BilVideo: A Video Database Management System
IEEE MultiMedia
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Fourier Descriptors for Plane Closed Curves
IEEE Transactions on Computers
Comparison of texture features based on Gabor filters
IEEE Transactions on Image Processing
ColorsInMotion: interactive visualization and exploration of video spaces
Proceedings of the 13th International MindTrek Conference: Everyday Life in the Ubiquitous Era
A web service platform for web-accessible archaeological databases
ISCIS'05 Proceedings of the 20th international conference on Computer and Information Sciences
Web image retrieval refinement by visual contents
WAIM '06 Proceedings of the 7th international conference on Advances in Web-Age Information Management
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The growing prevalence of multimedia systems is bringing the need for efficient techniques for storing and retrieving images into and from a database. In order to satisfy the information need of the users, it is of vital importance to effectively and efficiently adapt the retrieval process to each user. Considering this fact, an application for querying the images via their color, shape, and texture features in order to retrieve the similar salient objects is proposed. The features employed in content-based retrieval are most often simple low-level representations, while a human observer judges similarity between images based on high-level semantic properties. Using color, shape, and texture as an example, we show that a more accurate description of the underlying distribution of low-level features improves the retrieval quality. The performance experiments show that our application is effective in retrieval quality and has low processing cost.