Indoor-Outdoor Image Classification
CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
The Journal of Machine Learning Research
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Pictorial Structures for Object Recognition
International Journal of Computer Vision
Spatial Priors for Part-Based Recognition Using Statistical Models
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'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
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Hierarchical Field Framework for Unified Context-Based Classification
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
Factor Graphs for Region-based Whole-scene Classification
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Fuzzy spatial relationships for image processing and interpretation: a review
Image and Vision Computing
Image classification for content-based indexing
IEEE Transactions on Image Processing
Hi-index | 0.00 |
Object categorization is an important problem in computer vision The bag-of-words approach has gained much research in object categorization, which has shown state-of-art performance This bag-of-words(BOW) approach ignores spatial relationship between local features But local features in most classes have spatial dependence in real world So we propose a novel object categorization model with implicit local spatial relationship based on bag-of-words model(BOW with ILSR) The model use neighbor features of one local feature as its implicit local spatial relationship, which is integrated with its appearance feature to form two sources of information for categorization The characteristic of the model can not only preserve some degree of flexibility, but also incorporate necessary spatial information The algorithm is applied in Caltech-101 and Caltech-256 datasets to validate its efficiency The experimental results show its good performance.