Multilevel Chinese takeaway process and label-based processes for rule induction in the context of automated sports video annotation

We propose four variants of a novel hierarchical hidden Markov models strategy for rule induction in the context of automated sports video annotation including a multilevel Chinese takeaway process (MLCTP) based on the Chinese restaurant process and a novel Cartesian product label-based hierarchical bottom-up clustering (CLHBC) method that employs prior information contained within label structures. Our results show significant improvement by comparison against the flat Markov model: optimal performance is obtained using a hybrid method, which combines the MLCTP generated hierarchical topological structures with CLHBC generated event labels. We also show that the methods proposed are generalizable to other rule-based environments including human driving behavior and human actions

Progressive Probabilistic Hough Transform for line detection

We present a novel Hough Transform algorithm referred to as Progressive Probabilistic Hough Transform (PPHT). Unlike the Probabilistic HT where Standard HT is performed on a pre-selected fraction of input points, PPHT minimises the amount of computation needed to detect lines by exploiting the difference an the fraction of votes needed to detect reliably lines with different numbers of supporting points. The fraction of points used for voting need not be specified ad hoc or using a priori knowledge, as in the probabilistic HT; it is a function of the inherent complexity of the input data. The algorithm is ideally suited for real-time applications with a fixed amount of available processing time, since voting and line detection is interleaved. The most salient features are likely to be detected first. Experiments show that in many circumstances PPHT has advantages over the Standard HT

Food Recognition using Fusion of Classifiers based on CNNs

With the arrival of convolutional neural networks, the complex problem of food recognition has experienced an important improvement in recent years. The best results have been obtained using methods based on very deep convolutional neural networks, which show that the deeper the model,the better the classification accuracy will be obtain. However, very deep neural networks may suffer from the overfitting problem. In this paper, we propose a combination of multiple classifiers based on different convolutional models that complement each other and thus, achieve an improvement in performance. The evaluation of our approach is done on two public datasets: Food-101 as a dataset with a wide variety of fine-grained dishes, and Food-11 as a dataset of high-level food categories, where our approach outperforms the independent CNN models

Wearable face recognition aid

The feasibility of realising a low cost wearable face recognition aid based on a robust correlation algorithm is investigated. The aim of the study is to determine the limiting spatial and grey level resolution of the probe and gallery images that would support successful prompting of the identity of input face images. Low spatial and grey level resolution images are obtained from good quality image data algorithmically. The tests carried out on the XM2VTS database demonstrate that robust correlation is very resilient to degradations of spatial and grey level image resolution. Correct prompts have been generated in 98% cases even for severely degraded images

On-line Learning of Mutually Orthogonal Subspaces for Face Recognition by Image Sets

We address the problem of face recognition by matching image sets. Each set of face images is represented by a subspace (or linear manifold) and recognition is carried out by subspace-to-subspace matching. In this paper, 1) a new discriminative method that maximises orthogonality between subspaces is proposed. The method improves the discrimination power of the subspace angle based face recognition method by maximizing the angles between different classes. 2) We propose a method for on-line updating the discriminative subspaces as a mechanism for continuously improving recognition accuracy. 3) A further enhancement called locally orthogonal subspace method is presented to maximise the orthogonality between competing classes. Experiments using 700 face image sets have shown that the proposed method outperforms relevant prior art and effectively boosts its accuracy by online learning. It is shown that the method for online learning delivers the same solution as the batch computation at far lower computational cost and the locally orthogonal method exhibits improved accuracy. We also demonstrate the merit of the proposed face recognition method on portal scenarios of multiple biometric grand challenge

Decentralized learning with budgeted network load using Gaussian copulas and classifier ensembles

We examine a network of learners which address the same classification task but must learn from different data sets. The learners cannot share data but instead share their models. Models are shared only one time so as to preserve the network load. We introduce DELCO (standing for Decentralized Ensemble Learning with COpulas), a new approach allowing to aggregate the predictions of the classifiers trained by each learner. The proposed method aggregates the base classifiers using a probabilistic model relying on Gaussian copulas. Experiments on logistic regressor ensembles demonstrate competing accuracy and increased robustness in case of dependent classifiers. A companion python implementation can be downloaded at https://github.com/john-klein/DELC