Visual Query Expansion via Incremental Hypernetwork Models of Image and Text

Abstract. Humans can associate vision and language modalities and thus generate mental imagery, i.e. visual images, from linguistic input in an environment of unlimited inflowing information. Inspired by human memory, we separate a text-to-image retrieval task into two steps: 1) text-to-image conversion (generating visual queries for the 2 step) and 2) image-to-image retrieval task. This separation is advantageous for inner representation visualization, learning incremental dataset, using the results of content-based image retrieval. Here, we propose a visual query expansion method that simulates the capability of human associative memory. We use a hyperenetwork model (HN) that combines visual words and linguistic words. HNs learn the higher-order cross-modal associative relationships incrementally on a set of image-text pairs in sequence. An incremental HN generates images by assembling visual words based on linguistic cues. And we retrieve similar images with the generated visual que...

• Type: Journal
• Year: 2010
• Where: PRICAI
• Authors: Min-Oh Heo, Myunggu Kang, Byoung-Tak Zhang
• Keywords: Artificial Intelligence, Image, PRICAI 2010, Visual, Visual Query