Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
A Probabilistic Approach to Object Recognition Using Local Photometry and Global Geometry
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Learning to Detect Objects in Images via a Sparse, Part-Based Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Creating Efficient Codebooks for Visual Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Machine Learning
International Journal of Computer Vision
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
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
International Journal of Computer Vision
Towards optimal bag-of-features for object categorization and semantic video retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Putting Objects in Perspective
International Journal of Computer Vision
International Journal of Computer Vision
The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stages as Models of Scene Geometry
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic spatial context models for scene content understanding
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Sampling strategies for bag-of-features image classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Proceedings of the 20th ACM international conference on Multimedia
Machine Vision and Applications
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The visual extent of an object reaches beyond the object itself. This is a long standing fact in psychology and is reflected in image retrieval techniques which aggregate statistics from the whole image in order to identify the object within. However, it is unclear to what degree and how the visual extent of an object affects classification performance. In this paper we investigate the visual extent of an object on the Pascal VOC dataset using a Bag-of-Words implementation with (colour) SIFT descriptors.Our analysis is performed from two angles. (a) Not knowing the object location, we determine where in the image the support for object classification resides. We call this the normal situation. (b) Assuming that the object location is known, we evaluate the relative potential of the object and its surround, and of the object border and object interior. We call this the ideal situation. Our most important discoveries are: (i) Surroundings can adequately distinguish between groups of classes: furniture, animals, and land-vehicles. For distinguishing categories within one group the surroundings become a source of confusion. (ii) The physically rigid plane, bike, bus, car, and train classes are recognised by interior boundaries and shape, not by texture. The non-rigid animals dog, cat, cow, and sheep are recognised primarily by texture, i.e. fur, as their projected shape varies greatly. (iii) We confirm an early observation from human psychology (Biederman in Perceptual Organization, pp. 213---263, 1981): in the ideal situation with known object locations, recognition is no longer improved by considering surroundings. In contrast, in the normal situation with unknown object locations, the surroundings significantly contribute to the recognition of most classes.