Automatic concept detection is a crucial aspect of automatically indexing unstructured multimedia archives. However, the current prevalence of one-per-class detectors neglect inherent concept relation- ships and operate in isolation. This is insufficient when analyzing content gathered from wearable visual sensing, in which concepts occur with high diversity and with correlation depending on context. This paper presents a method to enhance concept detection results by constructing and factorizing a multi-way concept detection tensor in a time-aware manner. We derived a weighted non-negative tensor factorization algorithm and applied it to model concepts’ temporal occurrence patterns and show how it boosts overall detection performance. The potential of our method is demonstrated on lifelog datasets with varying levels of original concept detection accuracies.
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