INSTANCE-WEIGHTED MIXTURE MODELING TO ENHANCE TRAINING COLLECTIONS FOR IMAGE ANNOTATION

摘要

Automatic selection of training images is enhanced using an instance-weighted mixture modeling framework called ARTEMIS. An optimization algorithm is derived that in addition to mixture parameter estimation learns instance-weights, essentially adapting to the noise associated with each example. The mechanism of hypothetical local mapping is evoked so that data in diverse mathematical forms or modalities can be cohesively treated as the system maintains tractability in optimization. Training examples are selected from top-ranked images of a likelihood-based image ranking. Experiments indicate that ARTEMIS exhibits higher resilience to noise than several baselines for large training data collection. The performance of ARTEMIS-trained image annotation system is comparable to using manually curated datasets.

主权项

1. A method of improving the precision of training data used in automated image annotation, categorization, recognition, understanding, or retrieval, comprising the steps of:
providing a digital computer; receiving at the computer a plurality of previously tagged digital images; executing an algorithm on the computer to perform the following operations:
(a) extracting visual and textual features from the tagged images,(b) variably weighting the images based upon the extracted features,(c) computing a reference model for the images based on weighted instances through one or multiple iterations,(d) retaining images with high likelihood of correct tagging based upon the reference model; and using the retained images to train an automated image annotation, categorization, recognition, understanding, or retrieval system.