Foundations and Trends® in Computer Graphics and Vision
Robust Object Detection with Interleaved Categorization and Segmentation
International Journal of Computer Vision
Selecting features for object detection using an AdaBoost-compatible evaluation function
Pattern Recognition Letters
Learning an Alphabet of Shape and Appearance for Multi-Class Object Detection
International Journal of Computer Vision
Hierarchical learning of dominant constellations for object class recognition
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Generic object recognition using boosted combined features
RobVis'08 Proceedings of the 2nd international conference on Robot vision
Journal of Real-Time Image Processing
Sparse patch-histograms for object classification in cluttered images
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Object recognition using multiresolution trees
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Weakly supervised learning of part-based spatial models for visual object recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
A general learning framework for non-rigid image registration
Miar'06 Proceedings of the Third international conference on Medical Imaging and Augmented Reality
A boundary-fragment-model for object detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Keypoints derivation for object class detection with SIFT algorithm
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Efficient development of user-defined image recognition systems
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume Part I
A novel multiplex cascade classifier for pedestrian detection
Pattern Recognition Letters
Computer Vision and Image Understanding
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We combine local texture features (PCA-SIFT), global features (shape context), and spatial features within a single multi-layer AdaBoost model of object class recognition. The first layer selects PCA-SIFT and shape context features and combines the two feature types to form a strong classifier. Although previous approaches have used either feature type to train an AdaBoost model, our approach is the first to combine these complementary sources of information into a single feature pool and to use Adaboost to select those features most important for class recognition. The second layer adds to these local and global descriptions information about the spatial relationships between features. Through comparisons to the training sample, we first find the most prominent local features in Layer 1, then capture the spatial relationships between these features in Layer 2. Rather than discarding this spatial information, we therefore use it to improve the strength of our classifier. We compared our method to [4, 12, 13] and in all cases our approach outperformed these previous methods using a popular benchmark for object class recognition [4]. ROC equal error rates approached 99%. We also tested our method using a dataset of images that better equates the complexity between object and non-object images, and again found that our approach outperforms previous methods.