Dimensionality Reduction for Image Features using Deep Learning and Autoencoders (bibtex)
@InProceedings{PetscharnigSparDa2017, title = {Dimensionality Reduction for Image Features using Deep Learning and Autoencoders}, author = {Petscharnig, Stefan and Lux, Mathias and Chatzichristofis, Savvas}, booktitle = {15th International Workshop on Content-Based Multimedia Indexing}, year = {2017}, address = {New York, USA}, editor = {Bertini, Marco}, month = {jun}, pages = {.}, publisher = {ACM}, abstract = {The field of similarity based image retrieval has experienced a game changer lately. Hand crafted image features have been vastly outperformed by machine learning based approaches. Deep learning methods are very good at finding optimal features for a domain, given enough data is available to learn from. However, hand crafted features are still means to an end in domains, where the data either is not freely available, i.e. because it violates privacy, where there are commercial concerns, or where it cannot be transmitted, i.e. due to bandwidth limitations. Moreover, we have to rely on hand crafted methods whenever neural networks cannot be trained effectively, e.g. if there is not enough training data. In this paper, we investigate a particular approach to combine hand crafted features and deep learning to (i) achieve early fusion of off the shelf handcrafted global image features and (ii) reduce the overall number of dimensions to combine both worlds. This method allows for fast image retrieval in domains, where training data is sparse.}, doi = {10.1145/3095713.3095737}, isbn10 = {978-1-4503-5333-5}, language = {EN}, location = {Firenze, Italy}, talkdate = {2017.06.21}, talktype = {registered}, url = {https://dl.acm.org/citation.cfm?id=3095737} }
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