Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
On-line EM Algorithm for the Normalized Gaussian Network
Neural Computation
Talking robots with LEGO MindStorms
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Improving Automatic Image Annotation Based on Word Co-occurrence
Adaptive Multimedial Retrieval: Retrieval, User, and Semantics
Multiple Bernoulli relevance models for image and video annotation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
The learning of adjectives and nouns from affordance and appearance features
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
| Hi-index | 0.00 |
We study the problem of learning to recognise objects in the context of autonomous agents. We cast object recognition as the process of attaching meaningful concepts to specific regions of an image. In other words, given a set of images and their captions, the goal is to segment the image, in either an intelligent or naive fashion, then to find the proper mapping between words and regions. In this paper, we demonstrate that a model that learns spatial relationships between individual words not only provides accurate annotations, but also allows one to perform recognition that respects the real-time constraints of an autonomous, mobile robot.