PhD thesis from University of Oxford, Bibtex | Abstract | PDF | All O. M. Parkhi, E. Rahtu, Q. Cao, A. Zisserman Automated Video Face Labelling for Films and TV Material IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 42, Number 4, page , apr Originally named fasriger schwerspath by Andreas Gotthelf Schütz in Renamed schwefelsaurer strontianite aus Pennsylvania by Martin Klaproth in Renamed by Abraham Gottlieb Werner in in German zoelestin from the Greek cœlestis for celestial, in allusion to the faint blue color of the original specimen. Renamed Schützit by Dietrich Ludwig Gustav PhD thesis from University of Oxford, Bibtex | Abstract | PDF | All O. M. Parkhi, E. Rahtu, Q. Cao, A. Zisserman Automated Video Face Labelling for Films and TV Material IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 42, Number 4, page , apr
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In most cases, olga veksler phd thesis, these works may not olga veksler phd thesis reposted without the explicit permission of the copyright holder. We investigate the gains in precision and speed, that can be obtained by using Convolutional Networks ConvNets olga veksler phd thesis on-the-fly retrieval - where classifiers are learnt at run time for a textual query from downloaded images, and used to rank large image or video datasets. Consequently, olga veksler phd thesis, features with the state-of-the-art performance on large-scale datasets of millions of images can fit in the memory of even a commodity GPU card; iii we show that an SVM classifier can be learnt within a ConvNet framework on a GPU in parallel with downloading the new training images, allowing for a continuous refinement of the model as more images become available, and simultaneous training and ranking, olga veksler phd thesis.
The outcome is an on-the-fly system that significantly outperforms its predecessors in terms of: precision of retrieval, memory requirements, and speed, facilitating accurate on-the-fly learning and ranking in under a second on a single GPU. We start our investigation with pictorial structure models and propose an efficient method of model fitting using skin regions. To detect the skin, we learn a colour model locally from the image by detecting the facial region. The resulting olga veksler phd thesis detections are also used for hand localisation.
Our next contribution is a comprehensive dataset of 2D hand images. We collected this dataset from publicly available image sources, and annotated images with hand bounding boxes. The bounding boxes are not axis aligned, but are rather oriented with respect to the wrist.
Our dataset is quite exhaustive as it includes images of different hand shapes and layout configurations. Using our dataset, we train a hand detector that is robust to background clutter and lighting variations. Our hand detector is implemented as a two-stage system. The first stage involves proposing hand hypotheses using complementary image features, which are then evaluated by the second stage classifier.
This improves both precision and recall and results in a state-of-the-art hand detection method. In addition we develop a new method of non-maximum suppression based on super-pixels. We also contribute an efficient training algorithm for olga veksler phd thesis output ranking. In our algorithm, we reduce the time complexity of an expensive training component from quadratic to linear. This algorithm has a broad applicability and we use it for solving human layout estimation and taxonomic multiclass classification problems.
For human layout, we use different body part detectors to propose part candidates. These candidates are then combined and scored using our ranking algorithm. By applying this bottom-up approach, we achieve accurate human layout estimation despite variations in viewpoint and layout configuration.
In the multiclass classification problem, we define the misclassification error using a class taxonomy. The problem then reduces to a structured output ranking problem and we use our ranking method to optimise it. This allows inclusion of semantic knowledge about the classes and results in a more meaningful classification system.
Lastly, we substantiate our ranking algorithm with theoretical proofs and derive the generalisation bounds for it. These bounds prove that the training error reduces to the lowest possible error asymptotically. Mittal, M. Blaschko, A.
Zisserman, P, olga veksler phd thesis. Blaschko and Andrew Zisserman and Philip H, olga veksler phd thesis. Often objects might have some sort of structure such as a taxonomy in which the mis-classification score for object classes close by, using tree distance within the taxonomy, should be less than for those far olga veksler phd thesis. This is an example of multi-class classification in which the loss function has a special structure.
Another example in vision is for the ubiquitous pictorial structure or parts based model. In this case we would like the mis-classification score to be proportional to the number of parts misclassified, olga veksler phd thesis.
It transpires both of these are examples of structured output ranking problems. However, so far no efficient large scale algorithm for this problem has been demonstrated.
In this work we propose an algorithm for structured output ranking that can be trained in a time linear in the number of samples under a mild assumption common to many computer vision problems: that the loss function can be discretized olga veksler phd thesis a small number of values.
We show the feasibility of structured ranking on these two core computer vision problems and demonstrate a consistent and substantial improvement over competing techniques.
Aside from this, we also achieve state-of-the art results for the PASCAL VOC human layout problem. Parkhi, A. Vedaldi, A. Zisserman, C. Parkhi and Andrea Vedaldi and Andrew Zisserman and C. To this end we introduce a new annotated dataset of pets, the Oxford-IIIT-Pet datasetcovering 37 different breeds of cats and dogs. The visual problem is very challenging as these animals, particularly cats, are very deformable and there can be quite subtle differences between the breeds. We make a number of contributions: first, we introduce a model to classify a pet breed automatically from an image.
The model combines shape, captured by a deformable part model detecting the pet face, and appearance, captured by a bag-of-words model that describes the pet fur, olga veksler phd thesis. Fitting the model involves automatically segmenting the animal in the olga veksler phd thesis. Second, we compare two classification approaches: a hierarchical one, in which a pet is first assigned to the cat or dog family and then to a breed, and a flat one, in which the breed is obtained directly.
We also investigate a number of animal and image orientated spatial layouts. These models are very good: they beat olga veksler phd thesis previously published results on the challenging ASIRRA test cat vs dog discrimination.
The novelty is that the person of interest can be specified at run time by a text query, and a discriminative classifier for that person is then learnt on-the-fly using images downloaded from Google Image search.
The performance of the method is evaluated on a ground truth dataset of episodes of Scrubs, and results are also shown for retrieval on the TRECVid IACC. B dataset of over 8k videos. Olga veksler phd thesis entire process from specifying the query to receiving the ranked results takes only a matter of seconds. Patron-Perez, M. Marszałek, I. Reid, A.
Our approach is person-centric. As a first stage we track all upper bodies and heads in a video using a tracking-by-detection approach that combines detections with KLT tracking and clique partitioning, together with occlusion detection, to yield robust person tracks.
We develop local descriptors of activity based on the head orientation estimated using a set of pose-specific classifiers and the local spatiotemporal region around them, together with global descriptors that encode the relative positions of people as a function of interaction type. Learning and inference on the model uses a structured output SVM which combines the local and global descriptors in a principled manner.
Inference using the model yields information about which pairs of people are interacting, olga veksler phd thesis, their interaction class, and their head orientation which is also treated as a variable, enabling mistakes in the classifier to be corrected using global context, olga veksler phd thesis.
We show that inference can be carried out with polynomial complexity in the number of people, and describe an efficient algorithm for this. The method is evaluated on a new dataset comprising video clips acquired from 23 different TV shows and on the benchmark UT--Interaction dataset. Pfister, J. Charles, M. Everingham, A. Simonyan, A. We make a number of novel contributions towards this goal: first, it is shown that learning the pooling regions for the descriptor can be formulated as a convex optimisation problem selecting the regions using sparsity; second, it is shown that dimensionality reduction can olga veksler phd thesis be formulated as a convex optimisation problem, using the nuclear norm to reduce dimensionality.
Both of these problems use large margin discriminative learning methods. The third contribution is a new method of obtaining the positive and negative training data in a weakly supervised manner. And, finally, we employ a state-of-the-art stochastic optimizer that is efficient and well matched to the non-smooth cost functions proposed here.
It is demonstrated that the new learning methods improve over the state of the art in descriptor learning for large scale matching, Brown et al. PAMIand large scale object retrieval, Philbin et al. ECCV Simonyan, M. Modat, S. Ourselin, D. Cash, olga veksler phd thesis, A.
Criminisi, A. The retrieval system is demonstrated on MRI data from the ADNI dataset, and it is shown that the learnt ranking function outperforms the baseline, olga veksler phd thesis.
The method searches for self-similar image structures that form nonaccidental patterns, for example collinear arrangements. We demonstrate a simple implementation of this idea where self-similar structures are found by looking for SIFT descriptors that map to the same visual words in image-specific vocabularies. This results in a visual word map which olga veksler phd thesis searched for elongated connected components. Finally, segments are fitted to these connected components, extracting linear image structures beyond the ones that can be captured by conventional edge detectors, as the latter implicitly assume a specific appearance for the edges steps.
This is applied to the task of estimating vanishing points, horizon, and zenith in standard benchmark data, obtaining state-of-the-art results. We also propose a new vanishing point estimation algorithm based on recently introduced techniques for the continuous-discrete optimisation of energies arising from model selection priors.
While most constructions aim at obtaining low-dimensional and dense features, in this work we explore high-dimensional and sparse ones. We give a method to compute sparse features for arbitrary kernels, re-deriving as a special case a popular map for the intersection kernel and extending it to arbitrary additive kernels.
We show that bundleoptimisationmethodscanhandleefficientlythese sparse features in learning. As an application, we show that product quantisation can be interpreted as a sparse feature encoding, and use this to significantly accelerate learning with this technique. We demonstrate these ideas on image classification with Fisher kernels and object detection with deformable part models on the challenging PASCAL VOC data, obtaining five to ten-fold speed-ups as well as reducing memory use by an order of magnitude.
Whyte, J, olga veksler phd thesis. Sivic, A.
Kelly Ferber - Mid-Candidature Review of Doctoral Thesis
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Sep 15, · The ATC's mission is to further Bentley’s leadership in and strategic focus on the integration of business and technology. We enrich scholarly initiatives and student learning by empowering faculty with state-of-the-art academic, information, and communication resources Originally named fasriger schwerspath by Andreas Gotthelf Schütz in Renamed schwefelsaurer strontianite aus Pennsylvania by Martin Klaproth in Renamed by Abraham Gottlieb Werner in in German zoelestin from the Greek cœlestis for celestial, in allusion to the faint blue color of the original specimen. Renamed Schützit by Dietrich Ludwig Gustav PhD thesis from University of Oxford, Bibtex | Abstract | PDF | All O. M. Parkhi, E. Rahtu, Q. Cao, A. Zisserman Automated Video Face Labelling for Films and TV Material IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 42, Number 4, page , apr
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