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Self-tuned Visual Subclass Learning with Shared Samples An Incremental Approach
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. (Computer Vision)ORCID iD: 0000-0001-5211-6388
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
2013 (English)Article, review/survey (Other academic) Epub ahead of print
Abstract [en]

Computer vision tasks are traditionally defined and eval-uated using semantic categories. However, it is known to thefield that semantic classes do not necessarily correspondto a unique visual class (e.g. inside and outside of a car).Furthermore, many of the feasible learning techniques athand cannot model a visual class which appears consistentto the human eye. These problems have motivated the useof 1) Unsupervised or supervised clustering as a prepro-cessing step to identify the visual subclasses to be used ina mixture-of-experts learning regime. 2) Felzenszwalb etal. part model and other works model mixture assignmentwith latent variables which is optimized during learning 3)Highly non-linear classifiers which are inherently capableof modelling multi-modal input space but are inefficient atthe test time. In this work, we promote an incremental viewover the recognition of semantic classes with varied appear-ances. We propose an optimization technique which incre-mentally finds maximal visual subclasses in a regularizedrisk minimization framework. Our proposed approach uni-fies the clustering and classification steps in a single algo-rithm. The importance of this approach is its compliancewith the classification via the fact that it does not need toknow about the number of clusters, the representation andsimilarity measures used in pre-processing clustering meth-ods a priori. Following this approach we show both quali-tatively and quantitatively significant results. We show thatthe visual subclasses demonstrate a long tail distribution.Finally, we show that state of the art object detection meth-ods (e.g. DPM) are unable to use the tails of this distri-bution comprising 50% of the training samples. In fact weshow that DPM performance slightly increases on averageby the removal of this half of the data.

Place, publisher, year, edition, pages
2013.
National Category
Computer Systems
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-192293OAI: oai:DiVA.org:kth-192293DiVA: diva2:967491
Note

QC 20160912

Available from: 2016-09-08 Created: 2016-09-08 Last updated: 2016-09-12Bibliographically approved
In thesis
1. Visual Representations and Models: From Latent SVM to Deep Learning
Open this publication in new window or tab >>Visual Representations and Models: From Latent SVM to Deep Learning
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

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.
Series
TRITA-CSC-A, ISSN 1653-5723 ; 21
Keyword
Computer Vision, Machine Learning, Artificial Intelligence, Deep Learning, Learning Representation, Deformable Part Models, Discriminative Latent Variable Models, Convolutional Networks, Object Recognition, Object Detection
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-192289 (URN)978-91-7729-110-7 (ISBN)
External cooperation:
Public defence
2016-09-27, Kollegiesalen, Brinellvägen 8, KTH-huset, våningsplan 4, KTH Campus, Stockholm, 15:26 (English)
Opponent
Supervisors
Note

QC 20160908

Available from: 2016-09-08 Created: 2016-09-08 Last updated: 2016-09-09Bibliographically approved

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