Automatic image annotation and retrieval using cross-media relevance models
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Multimedia content processing through cross-modal association
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Automatic multimedia cross-modal correlation discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A review of text and image retrieval approaches for broadcast news video
Information Retrieval
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Bag-of-visual-words expansion using visual relatedness for video indexing
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Foundations and Trends in Information Retrieval
Logo retrieval with a contrario visual query expansion
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple Bernoulli relevance models for image and video annotation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Hypernetworks: A Molecular Evolutionary Architecture for Cognitive Learning and Memory
IEEE Computational Intelligence Magazine
Mental imagery for a conversational robot
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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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 query. The method is evaluated on 26 video clips of 'Thomas and Friends'. Experiments show the performance of successive image retrieval rate up to 98.1% with a single text cue. It shows the additional potential to generate the visual query with several text cues simultaneously.