Fast shared boosting for large-scale concept detection

Visual concept detection consists in assigning labels to an image or keyframe based on its semantic content. Visual concepts are usually learned from an annotated image or video database with a machine learning algorithm, posing this problem as a multiclass supervised learning task. Some practical issues appear when the number of concept grows, in particular in terms of available memory and computing time, both for learning and testing. To cope with these issues, we propose to use a multiclass boosting algorithm with feature sharing and reduce its computational complexity with a set of efficient improvements. For this purpose, we explore a limited part of the possible parameter space, by adequately injecting randomness into the crucial steps of our algorithm. This makes our algorithm able to handle a problem of classification with many classes in a reasonable time, thanks to a linear complexity with regards to the number of concepts considered as well as the number of feature and their size. The relevance of our algorithm is evaluated in the context of information retrieval, on the benchmark proposed into the ImageCLEF international evaluation campaign and shows competitive results.