The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
PicSOM—content-based image retrieval with self-organizing maps
Pattern Recognition Letters - Selected papers from the 11th scandinavian conference on image analysis
Learning a Sparse Representation for Object Detection
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CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
Object Class Recognition Using Multiple Layer Boosting with Heterogeneous Features
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A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generic Object Recognition with Boosting
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison of Affine Region Detectors
International Journal of Computer Vision
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
Local invariant feature detectors: a survey
Foundations and Trends® in Computer Graphics and Vision
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
Human computation: a survey and taxonomy of a growing field
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
HCII'11 Proceedings of the 14th international conference on Human-computer interaction: design and development approaches - Volume Part I
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ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Semantic annotation of image groups with self-organizing maps
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
Efficient annotation of image data sets for computer vision applications
Proceedings of the 1st International Workshop on Visual Interfaces for Ground Truth Collection in Computer Vision Applications
IEEE Transactions on Circuits and Systems for Video Technology
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Development processes for building image recognition systems are highly specialized and require expensive expert knowledge. Despite some effort in developing generic image recognition systems, use of computer vision technology is still restricted to experts. We propose a flexible image recognition system (FOREST), which requires no prior knowledge about the recognition task and allows non-expert users to build custom image recognition systems, which solve a specific recognition task defined by the user. It provides a simple-to-use graphical interface which guides users through a simple development process for building a custom recognition system. FOREST integrates a variety of feature descriptors which are combined in a classifier using a boosting approach to provide a flexible and adaptable recognition framework. The evaluation shows, that image recognition systems developed with this framework are capable of achieving high recognition rates.