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Understanding semantic similarity among images is the core of a wide range ofcomputer vision applications. An important step towards this goal is to collectand learn human perceptions. Interestingly, the semantic context of images isoften ambiguous as images can be perceived with emphasis on different aspects,which may be contradictory to each other.  In this paper, we present a method for learning the semantic similarity amongimages, inferring their latent aspects and embedding them into multi-spacescorresponding to their semantic aspects.  We consider the multi-embedding problem as an optimization function thatevaluates the embedded distances with respect to the qualitative clusteringqueries. The key idea of our approach is to collect and embed qualitativemeasures that share the same aspects in bundles. To ensure similarity aspectsharing among multiple measures, image classification queries are presented to,and solved by users. The collected image clusters are then converted intobundles of tuples, which are fed into our bundle optimization algorithm thatjointly infers the aspect similarity and multi-aspect embedding. Extensiveexperimental results show that our approach significantly outperformsstate-of-the-art multi-embedding approaches on various datasets, and scaleswell for large multi-aspect similarity measures.

Bundle Optimization for Multi-aspect Embedding