Seeking Affinity Structure: Strategies for Improving m-best Graph Matching

State-of-the-art methods for finding the m-best solutions to graph matching (QAP) rely on exclusion strategies. The k-th best solution is found by excluding all better ones from the search space. This provides diversity, a natural requirement for transforming a MAP problem into a m-best one. Since diversity enforces mode hopping, it is usually combined with a mode-approximation strategy such as marginalisation. However, these methods are generic insofar they do not incorporate the detailed structure of the problem at hand, i.e. the properties of the global affinity matrix which characterise the search space. Without this knowledge, it is thus hard to devise a practical criterion for choosing the next variable to clamp. In this paper, we propose several strategies to select the next variable to clamp, spanning the whole range between depth-first and breadth-first search, and we contribute with a unifying view for characterising the search space on the fly. Our strategies are: a) Number of factors in which the variables participate, b) centrality measures associated with the affinity matrix, and c) discrete pooling. Our experiments show that max number of factors and centrality provide a trade-off between efficiency and accuracy, whereas discrete pooling leads to an improvement of the state-of-the-art

Saving Our Bacon: Applications of Deep Learning for Precision Pig Farming

PhD ThesisThe research presented in this thesis focussed on how deep learning can be applied to the field of agriculture to enable precision livestock farming for pigs. This refers to the use of technology to automatically monitor, predict, and manage livestock. Increased consumer awareness of the welfare issues facing animals in the farming industry, combined with growing demand for high-quality produce, has resulted in a need for providing farmers with tools to improve and simplify animal care. The concept of precision livestock farming tackles these requirements, as it makes it possible to treat animals as individuals, rather than as batches. This translates to tailored care for each animal and the potential for higher-quality produce. As deep learning has shown rapidly increasing potential in recent years, this research explored and evaluated various architectures for applications in two distinct areas within pig farming. We began by demonstrating how deep learning methods can be used to monitor and model the environmental conditions in which pigs are living in order to forecast oncoming respiratory disease. Implementing this approach can mean earlier intervention than if simplify looking for clinical symptoms. However, as not all diseases are caused by environmental conditions, we also implemented and evaluated a full workflow for the localisation and tracking of individual pigs. This made it possible to extract behavioural metrics to better understand the wellbeing of each pig. Overall, this research shows that deep learning can be used to advance the agriculture industry towards better levels of care, which is valuable for all stakeholders

Joint probabilistic matching using m-best solutions

Matching between two sets of objects is typically approached by finding the object pairs that collectively maximize the joint matching score. In this paper, we argue that this single solution does not necessarily lead to the optimal matching accuracy and that general one-to-one assignment problems can be improved by considering multiple hypotheses before computing the final similarity measure. To that end, we propose to utilize the marginal distributions for each entity. Previously, this idea has been neglected mainly because exact marginalization is intractable due to a combinatorial number of all possible matching permutations. Here, we propose a generic approach to efficiently approximate the marginal distributions by exploiting the m-best solutions of the original problem. This approach not only improves the matching solution, but also provides more accurate ranking of the results, because of the extra information included in the marginal distribution. We validate our claim on two distinct objectives: (i) person re-identification and temporal matching modeled as an integer linear program, and (ii) feature point matching using a quadratic cost function. Our experiments confirm that marginalization indeed leads to superior performance compared to the single (nearly) optimal solution, yielding state-of-the-art results in both applications on standard benchmarks.Seyed Hamid Rezatofighi, Anton Milani, Zhen Zhang, Qinfeng Shi, Anthony Dick, Ian Rei