Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contextual Priming for Object Detection
International Journal of Computer Vision
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Discovering Colocation Patterns from Spatial Data Sets: A General Approach
IEEE Transactions on Knowledge and Data Engineering
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
Multi-Image Matching Using Multi-Scale Oriented Patches
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Sparse Texture Representation Using Local Affine Regions
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Categorization by Learned Universal Visual Dictionary
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Q-learning of sequential attention for visual object recognition from informative local descriptors
ICML '05 Proceedings of the 22nd international conference on Machine learning
A Comparison of Affine Region Detectors
International Journal of Computer Vision
International Journal of Computer Vision
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Using Language to Drive the Perceptual Grouping of Local Image Features
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
2006 Special Issue: Modeling attention to salient proto-objects
Neural Networks
Robust Object Recognition with Cortex-Like Mechanisms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Accurate Image Search Using the Contextual Dissimilarity Measure
IEEE Transactions on Pattern Analysis and Machine Intelligence
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Hi-index | 0.00 |
How can interest point detectors benefit from contextual cues? In this articles, we introduce a context-aware semi-local detector (CASL) framework to give a systematic answer with three contributions: (1) We integrate the context of interest points to recurrently refine their detections. (2) This integration boosts interest point detectors from the traditionally local scale to a semi-local scale to discover more discriminative salient regions. (3) Such context-aware structure further enables us to bring forward category learning (usually in the subsequent recognition phase) into interest point detection to locate category-aware, meaningful salient regions. Our CASL detector consists of two phases. The first phase accumulates multiscale spatial correlations of local features into a difference of contextual Gaussians (DoCG) field. DoCG quantizes detector context to highlight contextually salient regions at a semi-local scale, which also reveals visual attentions to a certain extent. The second phase locates contextual peaks by mean shift search over the DoCG field, which subsequently integrates contextual cues into feature description. This phase enables us to integrate category learning into mean shift search kernels. This learning-based CASL mechanism produces more category-aware features, which substantially benefits the subsequent visual categorization process. We conducted experiments in image search, object characterization, and feature detector repeatability evaluations, which reported superior discriminability and comparable repeatability to state-of-the-art works.