Abstract
One of the grand challenges of machine intelligence and pattern recognition for the past decade has been bridging the semantic gap, that is, determining how to translate the low-level features from images, video and audio to the high-level concepts of humans. Concept detection is an important approach toward bridging the semantic gap by allowing computers to understand imagery using the conceptual vocabulary of humans. By exploiting big data, the current generation of algorithms has contributed and developed both advances in accuracy and computational efficiency as well as new paradigms and techniques in concept detection. This special issue provides a focus on the state-of-the-art in concept detection with big data. We received 21 submissions of which 16 were selected for the triple peer-review process. In this special issue, we are pleased to present six research papers on concept detection with big data that present the latest advances in this field:
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