Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
International Journal of Computer Vision
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Ontology-enriched semantic space for video search
Proceedings of the 15th international conference on Multimedia
Image tag clarity: in search of visual-representative tags for social images
WSM '09 Proceedings of the first SIGMM workshop on Social media
Semantic context transfer across heterogeneous sources for domain adaptive video search
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Inferring semantic concepts from community-contributed images and noisy tags
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Concept detectors: how good is good enough?
MM '09 Proceedings of the 17th ACM international conference on Multimedia
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
Towards a universal detector by mining concepts with small semantic gaps
Proceedings of the international conference on Multimedia
Can High-Level Concepts Fill the Semantic Gap in Video Retrieval? A Case Study With Broadcast News
IEEE Transactions on Multimedia
Hi-index | 12.05 |
Can we have a universal detector that could visually recognize unseen objects with no training exemplars available? Such a detector is so desirable, as there are hundreds of thousands of object concepts in human vocabulary but few labeled image examples available. In this study, we attempt to build such a universal detector to predict concepts in the absence of training data. First, by considering both semantic relatedness and visual variance, we mine a set of realistic small-semantic-gap (SSG) concepts from a large-scale image corpus, i.e., ImageNet, which comprises 4961 concepts and nearly 4 million images. The discovered SSG concepts can be depicted well by visual models and their detectors can deliver reasonably satisfactory recognition accuracies. From these distinctive visual models, we then leverage the semantic ontology knowledge and co-occurrence statistics of concepts to extend visual recognition to unseen concepts. The rational is that object concepts generally co-occur in a real-life image. Their visual co-occurrence and semantic ontology provide the possibility for concept recognition to transcend the visual learning of image exemplars, and therefore, enable the detector to predict unseen realistic concepts without training samples. To the best of our knowledge, this work presents the first research attempting to substantiate the semantic gap measuring of a large amount of concepts and leverage visually learnable concepts to predicate those with no training images available. Testings on NUS-WIDE dataset demonstrate that the selected concepts with small semantic gaps can be well modeled and the prediction of unseen concepts delivers promising results with comparable accuracy to preliminary training-based methods.