Temporal event clustering for digital photo collections

Abstract

Organizing digital photograph collections according to events such as holiday gatherings or vacations is a common practice among photographers. To support photographers in this task, we present similarity-based methods to cluster digital photos by time and image content. The approach is general and unsupervised, and makes minimal assumptions regarding the structure or statistics of the photo collection. We present several variants of an automatic unsupervised algorithm to partition a collection of digital photographs based either on temporal similarity alone, or on temporal and content-based similarity. First, interphoto similarity is quantified at multiple temporal scales to identify likely event clusters. Second, the final clusters are determined according to one of three clustering goodness criteria. The clustering criteria trade off computational complexity and performance. We also describe a supervised clustering method based on learning vector quantization. Finally, we review the results of an experimental evaluation of the proposed algorithms and existing approaches on two test collections.