Visual Representations and Models: From Latent SVM to Deep Learning
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Two important components of a visual recognition system are representation and model. Both involves the selection and learning of the features that are indicative for recognition and discarding those features that are uninformative. This thesis, in its general form, proposes different techniques within the frameworks of two learning systems for representation and modeling. Namely, latent support vector machines (latent SVMs) and deep learning.
First, we propose various approaches to group the positive samples into clusters of visually similar instances. Given a fixed representation, the sampled space of the positive distribution is usually structured. The proposed clustering techniques include a novel similarity measure based on exemplar learning, an approach for using additional annotation, and augmenting latent SVM to automatically find clusters whose members can be reliably distinguished from background class.
In another effort, a strongly supervised DPM is suggested to study how these models can benefit from privileged information. The extra information comes in the form of semantic parts annotation (i.e. their presence and location). And they are used to constrain DPMs latent variables during or prior to the optimization of the latent SVM. Its effectiveness is demonstrated on the task of animal detection.
Finally, we generalize the formulation of discriminative latent variable models, including DPMs, to incorporate new set of latent variables representing the structure or properties of negative samples. Thus, we term them as negative latent variables. We show this generalization affects state-of-the-art techniques and helps the visual recognition by explicitly searching for counter evidences of an object presence.
Following the resurgence of deep networks, in the last works of this thesis we have focused on deep learning in order to produce a generic representation for visual recognition. A Convolutional Network (ConvNet) is trained on a largely annotated image classification dataset called ImageNet with $\sim1.3$ million images. Then, the activations at each layer of the trained ConvNet can be treated as the representation of an input image. We show that such a representation is surprisingly effective for various recognition tasks, making it clearly superior to all the handcrafted features previously used in visual recognition (such as HOG in our first works on DPM). We further investigate the ways that one can improve this representation for a task in mind. We propose various factors involving before or after the training of the representation which can improve the efficacy of the ConvNet representation. These factors are analyzed on 16 datasets from various subfields of visual recognition.
Place, publisher, year, edition, pages
Stockholm, Sweden: KTH Royal Institute of Technology, 2016. , 172 p.
TRITA-CSC-A, ISSN 1653-5723 ; 21
Computer Vision, Machine Learning, Artificial Intelligence, Deep Learning, Learning Representation, Deformable Part Models, Discriminative Latent Variable Models, Convolutional Networks, Object Recognition, Object Detection
Electrical Engineering, Electronic Engineering, Information Engineering Computer Systems
Research subject Computer Science
IdentifiersURN: urn:nbn:se:kth:diva-192289ISBN: 978-91-7729-110-7OAI: oai:DiVA.org:kth-192289DiVA: diva2:967455
2016-09-27, Kollegiesalen, Brinellvägen 8, KTH-huset, våningsplan 4, KTH Campus, Stockholm, 15:26 (English)
Caputo, Barbara, Associate Professor
Carlsson, Stefan, Professor
QC 201609082016-09-082016-09-082016-09-09Bibliographically approved
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