Off-Line, Handwritten Numeral Recognition by Perturbation Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object-based visual attention for computer vision
Artificial Intelligence
Learning to Detect Objects in Images via a Sparse, Part-Based Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Object Categories from Google"s Image Search
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
VOCUS: A Visual Attention System for Object Detection and Goal-Directed Search (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence)
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
Robust Object Detection with Interleaved Categorization and Segmentation
International Journal of Computer Vision
Learning about objects with human teachers
Proceedings of the 4th ACM/IEEE international conference on Human robot interaction
Spatial relation model for object recognition in human-robot interaction
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Large-scale live active learning: Training object detectors with crawled data and crowds
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Real-time human pose recognition in parts from single depth images
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Large-scale image annotation using visual synset
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Knowledge-enabled decision making for robotic system utilizing ambient service components
Journal of Ambient Intelligence and Smart Environments - Ambient and Smart Component Technologies for Human Centric Computing
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We present a system architecture for domestic robots that allows them to learn object categories after one sample object was initially learned. We explore the situation in which a human teaches a robot a novel object, and the robot enhances such learning by using a large amount of image data from the Internet. The main goal of this research is to provide a robot with capabilities to enhance its learning while minimizing time and effort required for a human to train a robot. Our active learning approach consists of learning the object name using speech interface, and creating a visual object model by using a depth-based attention model adapted to the robot's personal space. Given the object's name (keyword), a large amount of object-related images from two main image sources (Google Images and the LabelMe website) are collected. We deal with the problem of separating good training samples from noisy images by performing two steps: (1) Similar image selection using a Simile Selector Classifier, and (2) non-real image filtering by implementing a variant of Gaussian Discriminant Analysis. After web image selection, object category classifiers are then trained and tested using different objects of the same category. Our experiments demonstrate the effectiveness of our robot learning approach.