Sean Moran

Real-Time Detection, Tracking, and Monitoring of Automatically Discovered Events in Social Media

We introduce ReDites, a system for realtime event detection, tracking, monitoring and visualisation. It is designed to assist Information Analysts in understanding and exploring complex events as they unfold in the world. Events are automatically detected from the Twitter stream. Then those that are categorised as being security-relevant are tracked, geolocated, summarised and visualised for the end-user. Furthermore, the system tracks changes in emotions over events, signalling possible flashpoints or abatement. We demonstrate the capabilities of ReDites using an extended use case from the September 2013 Westgate shooting incident. Through an evaluation of system latencies, we also show that enriched events are made available for users to explore within seconds of that event occurring.

Neighbourhood preserving quantisation for LSH

We introduce a scheme for optimally allocating multiple bits per hyperplane for Locality Sensitive Hashing (LSH). Existing approaches binarise LSH projections by thresholding at zero yielding a single bit per dimension. We demonstrate that this is a sub-optimal bit allocation approach that can easily destroy the neighbourhood structure in the original feature space. Our proposed method, dubbed Neighbourhood Preserving Quantization (NPQ), assigns multiple bits per hyperplane based upon adaptively learned thresholds. NPQ exploits a pairwise affinity matrix to discretise each dimension such that nearest neighbours in the original feature space fall within the same quantisation thresholds and are therefore assigned identical bits. NPQ is not only applicable to LSH, but can also be applied to any low-dimensional projection scheme. Despite using half the number of hyperplanes, NPQ is shown to improve LSH-based retrieval accuracy by up to 65% compared to the state-of-the-art.

Sparse Kernel Learning for Image Annotation

In this paper we introduce a sparse kernel learning framework for the Continuous Relevance Model (CRM). State-of-the-art image annotation models linearly combine evidence from several different feature types to improve image annotation accuracy. While previous authors have focused on learning the linear combination weights for these features, there has been no work examining the optimal combination of kernels. We address this gap by formulating a sparse kernel learning framework for the CRM, dubbed the SKL-CRM, that greedily selects an optimal combination of kernels. Our kernel learning framework rapidly converges to an annotation accuracy that substantially outperforms a host of state-of-the-art annotation models. We make two surprising conclusions: firstly, if the kernels are chosen correctly, only a very small number of features are required so to achieve superior performance over models that utilise a full suite of feature types; and secondly, the standard default selection of kernels commonly used in the literature is sub-optimal, and it is much better to adapt the kernel choice based on the feature type and image dataset.

A sparse kernel relevance model for automatic image annotation

In this paper, we introduce a new form of the continuous relevance model (CRM), dubbed the SKL-CRM, that adaptively selects the best performing kernel per feature type for automatic image annotation. Previous image annotation models apply a standard selection of kernels to model the distribution of image features. Popular examples include a Gaussian kernel for modelling GIST features or a Laplacian kernel for global colour histograms. In this work, we demonstrate that this standard assignment of kernels to feature types is sub-optimal and a substantially higher image annotation accuracy can be attained by adapting the kernel-feature assignment. We formulate an efficient greedy algorithm to find the best kernel-feature alignment and show that it is able to rapidly find a sparse subset of features that maximises annotation

Regularised Cross-Modal Hashing

F_{1}

Graph Regularised Hashing

F1 score. In a second contribution, we introduce two data-adaptive kernels for image annotation—the generalised Gaussian and multinomial kernels—which we demonstrate can better model the distribution of image features as compared to standard kernels. Evaluation is conducted on three standard image datasets across a selection of different feature representations. The proposed SKL-CRM model is found to attain performance that is competitive to a suite of state-of-the-art image annotation models.

Optimal kernel shape and bandwidth for atomistic support of continuum stress

In this paper we introduce a new form of the Continuous Relevance Model (the BSCRM) that captures the correlation between tags in a formal and consistent manner. We apply a beam search algorithm to find a near optimal set of mutually correlated tags for an image in a time that is linear in the depth of the search tree. We conduct an examination of the model performance under different kernels for the representation of the image feature distributions and suggest a method of adapting the kernel to the dataset. BS-CRM with a Minkowski kernel is found to significantly increase recall by 42% and precision by 38% over the original CRM model and outperforms more recent baselines on the standard Corel 5k dataset.

A Gaussian mixture modelling approach to the data-driven estimation of atomistic support for continuum stress

In this paper we propose Regularised Cross-Modal Hashing (RCMH) a new cross-modal hashing model that projects annotation and visual feature descriptors into a common Hamming space. RCMH optimises the hashcode similarity of related data-points in the annotation modality using an iterative three-step hashing algorithm: in the first step each training image is assigned a K-bit hashcode based on hyperplanes learnt at the previous iteration; in the second step the binary bits are smoothed by a formulation of graph regularisation so that similar data-points have similar bits; in the third step a set of binary classifiers are trained to predict the regularised bits with maximum margin. Visual descriptors are projected into the annotation Hamming space by a set of binary classifiers learnt using the bits of the corresponding annotations as labels. RCMH is shown to consistently improve retrieval effectiveness over state-of-the-art baselines.