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In this paper, we propose a novel formulation for multi-feature clustering using minimax optimization. To find a consensus clustering result that is agreeable to all feature modalities, our objective is to find a universal feature embedding, which not only fits each individual feature modality well, but also unifies different feature modalities by(More)
—The co-occurrence features are the composition of base features that have more discriminative power than individual base features. Although they show promising performance in visual recognition applications such as object, scene and action recognition, the discovery of optimal co-occurrence features is usually a computational demanding task. Unlike(More)
We propose a novel hierarchical sparse coding algorithm with spatial pooling and multi-feature fusion, to construct the low-level visual primitives, e.g., local image patches or regions , into high-level visual phrases, e.g., image patterns. In the first layer we learn the sparse codes for the visual primitives and then pass them into the second layer by(More)
Local feature extraction, coding, spatial pooling, and image classification are the four typical steps for state-of-the-art visual recognition systems. Unlike previous work that treats spatial pooling and image classification as separated steps, we propose to jointly learn the geometric pooling and image classifier such that class-specific geometric(More)
The co-occurrence features are the composition of base features that have more discriminative power than individual base features. Although they show promising performance in visual recognition applications such as object and scene recognition, the discovery of discriminative co-occurrence features is usually a computational demanding task. Unlike previous(More)
In multiple instance learning, the training set consists of labeled bags that include unlabeled instances, and the target is to predict the labels of unseen bags. A bag is labeled positive only if it contains at least one positive instance, otherwise it is a negative bag. Over the past years, many popular machine learning algorithms have been adapted to(More)
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