Fast multiresolution image querying
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Performance Characterization and Optimization for Image Retrieval
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Query by Visual Example - Content based Image Retrieval
EDBT '92 Proceedings of the 3rd International Conference on Extending Database Technology: Advances in Database Technology
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Content-based image retrieval: approaches and trends of the new age
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
A survey of content-based image retrieval with high-level semantics
Pattern Recognition
Streetscenes: towards scene understanding in still images
Streetscenes: towards scene understanding in still images
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
The usability of semantic search tools: A review
The Knowledge Engineering Review
Sketch2Photo: internet image montage
ACM SIGGRAPH Asia 2009 papers
Content-based image retrieval by indexing random subwindows with randomized trees
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Bridging the Gap: Query by Semantic Example
IEEE Transactions on Multimedia
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With increasingly large image databases, searching in them becomes an ever more difficult endeavor. Consequently, there is a need for advanced tools for image retrieval in a webscale context. Searching by tags becomes intractable in such scenarios as large numbers of images will correspond to queries such as "car and house and street". We present a novel approach that allows a user to search for images based on semantic sketches that describe the desired composition of the image. Our system operates on images with labels for a few high-level object categories, allowing us to search very fast with a minimal memory footprint. We employ a structure similar to random decision forests which avails a data-driven partitioning of the image space providing a search in logarithmic time with respect to the number of images. This makes our system applicable for large scale image search problems. We performed a user study that demonstrates the validity and usability of our approach.