A unified context assessing model for object categorization

Authors:
Lei Yang;Nanning Zheng;Jie Yang
Affiliations:
Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, China;Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, China;School of Computer Science, Carnegie Mellon University, USA
Venue:
Computer Vision and Image Understanding
Year:
2011

Citing 15
Cited 0

Hierarchical mixtures of experts and the EM algorithm

Neural Computation
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope

International Journal of Computer Vision
Contextual Priming for Object Detection

International Journal of Computer Vision
A Hierarchical Field Framework for Unified Context-Based Classification

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Estimating the Support of a High-Dimensional Distribution

Neural Computation
A Critical View of Context

International Journal of Computer Vision
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)

Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study

International Journal of Computer Vision
Randomized Clustering Forests for Image Classification

IEEE Transactions on Pattern Analysis and Machine Intelligence
Beyond Nouns: Exploiting Prepositions and Comparative Adjectives for Learning Visual Classifiers

ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Learning Spatial Context: Using Stuff to Find Things

ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Determining Patch Saliency Using Low-Level Context

ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context

International Journal of Computer Vision
Context based object categorization: A critical survey

Computer Vision and Image Understanding

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we investigate how to appropriately use contextual information for object categorization tasks, especially in the statistical bag-of-features (BoF) framework. Our main contributions are two-folds. Firstly, we propose a context measuring mechanism which could explicitly assess roles of context features for different object recognition tasks. By analyzing information entropy and data ambiguity, only the useful and confident context information would have a final impact on categorization. Secondly, based on the context assessing results, under the BoF framework, we design unified object representations that incorporate the object appearance and contextual information from multiple spatial levels without the need of prior scene segmentations or context annotations. We evaluate the proposed method by the PASCAL object categorization task. The experimental results demonstrate that the proposed context modeling approach improves object categorization significantly and outperforms several state-of-the-art context models.