Generic Object Recognition with Boosting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning spatial relations in object recognition
Pattern Recognition Letters
Foundations and Trends® in Computer Graphics and Vision
EURASIP Journal on Embedded Systems - Special issue on design and architectures for signal and image processing
Shape classification based on skeleton path similarity
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
Dictionary learning based object detection and counting in traffic scenes
Proceedings of the Fourth ACM/IEEE International Conference on Distributed Smart Cameras
Improving a discriminative approach to object recognition using image patches
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Object localization with boosting and weak supervision for generic object recognition
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
Visual classification of images by learning geometric appearances through boosting
ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
| Hi-index | 0.00 |
A new method is presented to learn object categories from unlabeled and unsegmented images for generic object recognition. We assume that each object can be characterized by a set of typical regions, and use a new segmentation method - "Similarity-Measure Segmentation" - to split the images into regions of interest. This approach may also deliver segments, which are split into several disconnected parts, which turns out to be a powerful description of local similarities. Several textural features are calculated for each region, which are used to learn object categories with Boosting. We demonstrate the flexibility and power of our method by excellent results on various datasets. In comparison, our recognition results are significantly higher than results published in related work.