Persistent Evidence of Local Image Properties in Generic ConvNets
2015 (English)In: Image Analysis: 19th Scandinavian Conference, SCIA 2015, Copenhagen, Denmark, June 15-17, 2015. Proceedings / [ed] Paulsen, Rasmus R., Pedersen, Kim S., Springer Publishing Company, 2015, 249-262 p.Conference paper (Refereed)
Supervised training of a convolutional network for object classification should make explicit any information related to the class of objects and disregard any auxiliary information associated with the capture of the image or thevariation within the object class. Does this happen in practice? Although this seems to pertain to the very final layers in the network, if we look at earlier layers we find that this is not the case. Surprisingly, strong spatial information is implicit. This paper addresses this, in particular, exploiting the image representation at the first fully connected layer,i.e. the global image descriptor which has been recently shown to be most effective in a range of visual recognition tasks. We empirically demonstrate evidences for the finding in the contexts of four different tasks: 2d landmark detection, 2d object keypoints prediction, estimation of the RGB values of input image, and recovery of semantic label of each pixel. We base our investigation on a simple framework with ridge rigression commonly across these tasks,and show results which all support our insight. Such spatial information can be used for computing correspondence of landmarks to a good accuracy, but should potentially be useful for improving the training of the convolutional nets for classification purposes.
Place, publisher, year, edition, pages
Springer Publishing Company, 2015. 249-262 p.
, Image Processing, Computer Vision, Pattern Recognition, and Graphics, 9127
IdentifiersURN: urn:nbn:se:kth:diva-172140DOI: 10.1007/978-3-319-19665-7_21ScopusID: 2-s2.0-84947982864OAI: oai:DiVA.org:kth-172140DiVA: diva2:845957
Scandinavian Conference on Image Analysis, Copenhagen, Denmark, 15-17 June, 2015
Qc 201508282015-08-132015-08-132015-08-28Bibliographically approved