Graph-based semi-supervised learning with multi-modality propagation for large-scale image datasets

Semi-supervised learning (SSL) is widely-used to explore the vast amount of unlabeled data in the world. Over the decade, graph-based SSL becomes popular in automatic image annotation due to its power of learning globally based on local similarity. However, recent studies have shown that the emergence of large-scale datasets challenges traditional methods. On the other hand, most previous works have concentrated on single-label annotation, which may not describe image contents well. To remedy the deficiencies, this paper proposes a new graph-based SSL technique with multi-label propagation, leveraging the distributed computing power of the MapReduce programming model. For high learning performance, the paper further presents both a multi-layer learning structure and a tag refinement approach, where the former unifies both visual and textual information of image data during learning, while the latter simultaneously suppresses noisy tags and emphasizes the other tags after learning. Experimental results based on a medium-scale and a large-scale image datasets show the effectiveness of the proposed methods.

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► We implement a multi-layer learning structure to unify various image features.
► We present a scalable implementation of multi-layer multi-label propagation.
► We present to automatically set the convergence criterion of label propagation.
► We present to effectively refine image tags after label propagation.