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- Aghazadeh, Omid, 1982-
(författare)
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Data Driven Visual Recognition
- 2014
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis is mostly about supervised visual recognition problems. Based on a general definition of categories, the contents are divided into two parts: one which models categories and one which is not category based. We are interested in data driven solutions for both kinds of problems.In the category-free part, we study novelty detection in temporal and spatial domains as a category-free recognition problem. Using data driven models, we demonstrate that based on a few reference exemplars, our methods are able to detect novelties in ego-motions of people, and changes in the static environments surrounding them.In the category level part, we study object recognition. We consider both object category classification and localization, and propose scalable data driven approaches for both problems. A mixture of parametric classifiers, initialized with a sophisticated clustering of the training data, is demonstrated to adapt to the data better than various baselines such as the same model initialized with less subtly designed procedures. A nonparametric large margin classifier is introduced and demonstrated to have a multitude of advantages in comparison to its competitors: better training and testing time costs, the ability to make use of indefinite/invariant and deformable similarity measures, and adaptive complexity are the main features of the proposed model.We also propose a rather realistic model of recognition problems, which quantifies the interplay between representations, classifiers, and recognition performances. Based on data-describing measures which are aggregates of pairwise similarities of the training data, our model characterizes and describes the distributions of training exemplars. The measures are shown to capture many aspects of the difficulty of categorization problems and correlate significantly to the observed recognition performances. Utilizing these measures, the model predicts the performance of particular classifiers on distributions similar to the training data. These predictions, when compared to the test performance of the classifiers on the test sets, are reasonably accurate.We discuss various aspects of visual recognition problems: what is the interplay between representations and classification tasks, how can different models better adapt to the training data, etc. We describe and analyze the aforementioned methods that are designed to tackle different visual recognition problems, but share one common characteristic: being data driven.
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| 3. |
- Aghazadeh, Omid, 1982-, et al.
(författare)
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Mixture component identification and learning for visual recognition
- 2012
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Ingår i: Computer Vision – ECCV 2012. - Berlin, Heidelberg : Springer. - 9783642337826 ; , s. 115-128
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Konferensbidrag (refereegranskat)abstract
- The non-linear decision boundary between object and background classes - due to large intra-class variations - needs to be modelled by any classifier wishing to achieve good results. While a mixture of linear classifiers is capable of modelling this non-linearity, learning this mixture from weakly annotated data is non-trivial and is the paper's focus. Our approach is to identify the modes in the distribution of our positive examples by clustering, and to utilize this clustering in a latent SVM formulation to learn the mixture model. The clustering relies on a robust measure of visual similarity which suppresses uninformative clutter by using a novel representation based on the exemplar SVM. This subtle clustering of the data leads to learning better mixture models, as is demonstrated via extensive evaluations on Pascal VOC 2007. The final classifier, using a HOG representation of the global image patch, achieves performance comparable to the state-of-the-art while being more efficient at detection time.
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| 4. |
- Aghazadeh, Omid, 1982-, et al.
(författare)
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Novelty Detection from an Ego-Centric perspective
- 2011
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Ingår i: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. - 9781457703942 ; , s. 3297-3304
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Konferensbidrag (refereegranskat)abstract
- This paper demonstrates a system for the automatic extraction of novelty in images captured from a small video camera attached to a subject's chest, replicating his visual perspective, while performing activities which are repeated daily. Novelty is detected when a (sub)sequence cannot be registered to previously stored sequences captured while performing the same daily activity. Sequence registration is performed by measuring appearance and geometric similarity of individual frames and exploiting the invariant temporal order of the activity. Experimental results demonstrate that this is a robust way to detect novelties induced by variations in the wearer's ego-motion such as stopping and talking to a person. This is an essentially new and generic way of automatically extracting information of interest to the camera wearer and can be used as input to a system for life logging or memory support.
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| 5. |
- Aghazadeh, Omid, 1982-, et al.
(författare)
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Properties of Datasets Predict the Performance of Classifiers
- 2013
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Ingår i: BMVC 2013 - Electronic Proceedings of the British Machine Vision Conference 2013. - : British Machine Vision Association, BMVA.
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Konferensbidrag (refereegranskat)abstract
- It has been shown that the performance of classifiers depends not only on the number of training samples, but also on the quality of the training set [10, 12]. The purpose of this paper is to 1) provide quantitative measures that determine the quality of the training set and 2) provide the relation between the test performance and the proposed measures. The measures are derived from pairwise affinities between training exemplars of the positive class and they have a generative nature. We show that the performance of the state of the art methods, on the test set, can be reasonably predicted based on the values of the proposed measures on the training set. These measures open up a wide range of applications to the recognition community enabling us to analyze the behavior of the learning algorithms w.r.t the properties of the training data. This will in turn enable us to devise rules for the automatic selection of training data that maximize the quantified quality of the training set and thereby improve recognition performance.
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