Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Training products of experts by minimizing contrastive divergence
Neural Computation
The Journal of Machine Learning Research
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Image retrieval on large-scale image databases
Proceedings of the 6th ACM international conference on Image and video retrieval
Multilayer pLSA for multimodal image retrieval
Proceedings of the ACM International Conference on Image and Video Retrieval
Deep networks for audio event classification in soccer videos
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Topic models for semantics-preserving video compression
Proceedings of the international conference on Multimedia information retrieval
New trends and ideas in visual concept detection: the MIR flickr retrieval evaluation initiative
Proceedings of the international conference on Multimedia information retrieval
Deep adaptive networks for image classification
ICIMCS '10 Proceedings of the Second International Conference on Internet Multimedia Computing and Service
Lost in binarization: query-adaptive ranking for similar image search with compact codes
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Discriminative deep belief networks for visual data classification
Pattern Recognition
Bilinear deep learning for image classification
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Semiconducting bilinear deep learning for incomplete image recognition
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
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Currently there are hundreds of millions (high-quality) images in online image repositories such as Flickr. This makes is necessary to develop new algorithms that allow for searching and browsing in those large-scale databases. In this work we explore deep networks for deriving a low-dimensional image representation appropriate for image retrieval. A deep network consisting of multiple layers of features aims to capture higher order correlations between basic image features. We will evaluate our approach on a real world large-scale image database and compare it to image representations based on topic models. Our results show the suitability of the approach for very large databases.