Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
International Journal of Computer Vision
Fast multiresolution image querying
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Photobook: content-based manipulation of image databases
International Journal of Computer Vision
VisualSEEk: a fully automated content-based image query system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
An automatic hierarchical image classification scheme
MULTIMEDIA '98 Proceedings of the sixth ACM international conference on Multimedia
Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
Comparing images using joint histograms
Multimedia Systems - Special issue on video content based retrieval
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Configuration based scene classification and image indexing
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A General Framework for Object Detection
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
MIRO'95 Proceedings of the Final conference on Multimedia Information Retrieval
IEEE Transactions on Image Processing
Using dual cascading learning frameworks for image indexing
VIP '05 Proceedings of the Pan-Sydney area workshop on Visual information processing
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With rapid advances in sensor, storage, processor, and communication technologies, consumers can now afford to create, store, process, and share large digital photo collections. With more and more digital photos accumulated, consumers need effective and efficient tools to index and retrieve relevant photos. In this paper, we propose a novel image representation called Visual Keyword Histogram (VKH) for content-based indexing and retrieval. Visual keywords are domain-relevant visual prototypes (e.g. faces, foliage, buildings etc) with both perceptual appearance and textual semantics. Collectively, VKHs are computed over spatial tessellation to represent the distribution of visual keywords in various parts of an image. To construct a vocabulary of visual keywords, an incremental neural network is adopted to learn visual keywords from examples. This allows us to build domain-specific visual vocabularies rapidly and incrementally. We demonstrate our approach on 2400 home photos with 15 semantic queries.