International Journal of Computer Vision
Digital image processing
Image Processing: The Fundamentals
Image Processing: The Fundamentals
Combining Textual and Visual Cues for Content-Based Image Retrieval on the World Wide Web
CBAIVL '98 Proceedings of the IEEE Workshop on Content - Based Access of Image and Video Libraries
Interactive Learning with a "Society of Models"
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Synobins: an intermediate level towards annotation and semantic retrieval
EURASIP Journal on Applied Signal Processing
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In this paper, we propose a new type of image feature, which consists of patterns of colors and intensities that capture the latent associations among images and primitive features in such a way that the noise and redundancy are minimized. Incorporating our feature model into a Content-based Image Retrieval (CBIR) system moves the research in image retrieval beyond simple matching of images based on their primitive features and creates a ground for learning image semantics from visual content. A system developed using our proposed feature model, will have the capability of learning associations between not only semantic concepts and images, but also between semantic concepts and patterns. We evaluated the performance of our system based on the retrieval accuracy and on the perceptual similarity order among retrieved images. When compared to standard image retrieval methods, our preliminary results show that, even if the feature space was reduced to a significantly lower dimensional space, the accuracy and perceptual similarity for our system remain the same or better depending on the category of images.