Robust and Efficient Detection of Salient Convex Groups
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Shared Features for Scalable Appearance-Based Object Recognition
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
Hierarchical Part-Based Visual Object Categorization
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Discovering Objects and their Localization in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Integrating Representative and Discriminative Models for Object Category Detection
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Spatial Weighting for Bag-of-Features
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Weakly Supervised Scale-Invariant Learning of Models for Visual Recognition
International Journal of Computer Vision
Representing shape with a spatial pyramid kernel
Proceedings of the 6th ACM international conference on Image and video retrieval
Groups of Adjacent Contour Segments for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scene Classification Using a Hybrid Generative/Discriminative Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object detection by contour segment networks
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Combining color and shape information for illumination-viewpoint invariant object recognition
IEEE Transactions on Image Processing
Efficient Hypothesis Generation through Sub-categorization for Multiple Object Detection
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Object Detection and Localization in Clutter Range Images Using Edge Features
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
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The objective of this paper is to use computer vision to detect and localize multiple object within an image in the presence of a cluttered background, substantial occlusion and significant scale changes. Our approach consists of first generating a set of hypotheses for each object using a generative model (pLSA) with a bag of visual words representing each image. Then, the discriminative part verifies each hypothesis using a multi-class SVM classifier with merging features that combines both spatial shape and color appearance of an object. In the post-processing stage, environmental context information is used to improve the performance of the system. A combination of features and context information are used to investigate the performance on our local database. The best performance is obtained using object-specific weighted merging features and the context information. Our approach overcomes the limitations of some state of the art methods.