A distributed approach for large-scale classifier training and image classification
A visual concept network is constructed to measure the inter-concept similarity. Inter-related classifiers are trained jointly rather than independently. A distributed approach is developed for parallel image classification. An MPI-based framework is developed to train inter-related classifiers. We perform our experiments on image set with 1000 classes for algorithm evaluation.
visual concept network parallel computing large-scale image classification huge computational cost huge storage/memory cost inter-related classifier training