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» Optimizing Learning in Image Retrieval
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CVPR
2010
IEEE
14 years 6 months ago
Unsupervised Learning of Invariant Features Using Video
We present an algorithm that learns invariant features from real data in an entirely unsupervised fashion. The principal benefit of our method is that it can be applied without hu...
David Stavens, Sebastian Thrun
GECCO
2004
Springer
151views Optimization» more  GECCO 2004»
14 years 3 months ago
Discovery of Human-Competitive Image Texture Feature Extraction Programs Using Genetic Programming
In this paper we show how genetic programming can be used to discover useful texture feature extraction algorithms. Grey level histograms of different textures are used as inputs ...
Brian T. Lam, Victor Ciesielski
GECCO
2009
Springer
204views Optimization» more  GECCO 2009»
14 years 2 months ago
Combined structure and motion extraction from visual data using evolutionary active learning
We present a novel stereo vision modeling framework that generates approximate, yet physically-plausible representations of objects rather than creating accurate models that are c...
Krishnanand N. Kaipa, Josh C. Bongard, Andrew N. M...
IROS
2006
IEEE
107views Robotics» more  IROS 2006»
14 years 4 months ago
Image Mapping and Visual Attention on a Sensory Ego-Sphere
Abstract— The Sensory Ego-Sphere (SES) is a short-term memory for a robot in the form of an egocentric, tessellated, spherical, sensory-motor map of the robot’s locale. This pa...
Katherine Achim Fleming, Richard Alan Peters II, R...
MM
2003
ACM
133views Multimedia» more  MM 2003»
14 years 3 months ago
Geographic location tags on digital images
We describe an end-to-end system that capitalizes on geographic location tags for digital photographs. The World Wide Media eXchange (WWMX) database indexes large collections of i...
Kentaro Toyama, Ron Logan, Asta Roseway