TY - JOUR
AU - Ohbuchi, Ryutarou
AB - Unsupervised representation learning of unlabeled multimedia data is important yet challenging problem for their indexing, clustering, and retrieval. There have been many attempts to learn representation from a collection of unlabeled 2D images. In contrast, however, less attention has been paid to unsupervised representation learning for unordered sets of high-dimensional feature vectors, which are often used to describe multimedia data. One such example is set of local visual features to describe a 2D image. This paper proposes a novel algorithm called Feature Set Aggregator (FSA) for accurate and efficient comparison among sets of high-dimensional features. FSA learns representation, or embedding, of unordered feature sets via optimization using a combination of two training objectives, that are, set reconstruction and set embedding, carefully designed for set-to-set comparison. Experimental evaluation under three multimedia information retrieval scenarios using 3D shapes, 2D images, and text documents demonstrates efficacy as well as generality of the proposed algorithm.
TI - Feature set aggregator: unsupervised representation learning of sets for their comparison
JO - Multimedia Tools and Applications
DO - 10.1007/s11042-019-08078-y
DA - 2019-08-20
UR - https://www.deepdyve.com/lp/springer-journals/feature-set-aggregator-unsupervised-representation-learning-of-sets-8gpAd5i8kR
SP - 35157
EP - 35178
VL - 78
IS - 24
DP - DeepDyve
ER -