Convolutional neural networks (CNNs) with log-mel spectrum features have shown promising results for acoustic scene classification tasks. However, the performance of these CNN based classifiers is still lacking as they do not generalise well for unknown environments. To address this issue, we introduce an acoustic spectrum transformation network where traditional log-mel spectrums are transformed into imagined visual features (IVF). The imagined visual features are learned by exploiting the relationship between audio and visual features present in video recordings. An auto-encoder is used to encode images as visual features and a transformation network learns how to generate imagined visual features from log-mel. Our model is trained on a large dataset of Youtube videos. We test our proposed method on the scene classification task of DCASE and ESC-50, where our method outperforms other spectrum features, especially for unseen environments.

We propose a self-supervised method for image relighting of single view images in the wild. The method is based on an auto-encoder which deconstructs an image into two separate encodings, relating to the scene illumination and content, respectively. In order to disentangle this embedding information without supervision, we exploit the assumption that some augmentation operations do not affect the image content and only affect the direction of the light. A novel loss function, called spherical harmonic loss, is introduced that forces the illumination embedding to convert to a spherical harmonic vector. We train our model on large-scale datasets such as Youtube 8M and CelebA. Our experiments show that our method can correctly estimate scene illumination and realistically re-light input images, without any supervision or a prior shape model. Compared to supervised methods, our approach has similar performance and avoids common lighting artifacts.

On the one hand, mainly within the computer vision community, multi-resolution image labelling problems with pixel, super-pixel and object levels, have made great progress towards the modelling of holistic scene understanding. On the other hand, mainly within the robotics and graphics communities, multi-resolution 3Drepresentations of the world have matured to be efficient and accurate. In this paper we bring together the two hands and move towards the new direction of unified recognition, reconstruction and representation. We tackle the problem by embedding an octree into a hierarchical robust PNMarkov Random Field. This allows us to jointly infer the multi-resolution 3Dvolume along with the object-class labels, all within the constraints of an octree data-structure. The octree representation is chosen as this data-structure is efficient for further processing such as dynamic updates, data compression, and surface reconstruction. We perform experiments in inferring our semantic octree on the The kitti Vision Benchmark Suite in order to demonstrate its efficacy.

Semantic reconstruction of a scene is important for a variety of applications such as 3D modelling, object recognition and autonomous robotic navigation. However, most object labelling methods work in the image domain and fail to capture the information present in 3D space. In this work we propose a principled way to generate object labelling in 3D. Our method builds a triangulated meshed representation of the scene from multiple depth estimates. We then define a CRF over this mesh, which is able to capture the consistency of geometric properties of the objects present in the scene. In this framework, we are able to generate object hypotheses by combining information from multiple sources: geometric properties (from the 3D mesh), and appearance properties (from images). We demonstrate the robustness of our framework in both indoor and outdoor scenes. For indoor scenes we created an augmented version of the NYU indoor scene dataset (RGBD images) with object labelled meshes for training and evaluation. For outdoor scenes, we created ground truth object labellings for the KITTY odometry dataset (stereo image sequence). We observe a significant speed-up in the inference stage by performing labelling on the mesh, and additionally achieve higher accuracies.

In this paper we propose a robust algorithm that generates an efficient and accurate dense 3D reconstruction with associated semantic labellings. Intelligent autonomous systems require accurate 3D reconstructions for applications such as navigation and localisation. Such systems also need to recognise their surroundings in order to identify and interact with objects of interest. Considerable emphasis has been given to generating a good reconstruction but less effort has gone into generating a 3D semantic model. The inputs to our algorithm are street level stereo image pairs acquired from a camera mounted on a moving vehicle. The depth-maps, generated from the stereo pairs across time, are fused into a global 3D volume online in order to accommodate arbitrary long image sequences. The street level images are automatically labelled using a Conditional Random Field (CRF) framework exploiting stereo images, and label estimates are aggregated to annotate the 3D volume. We evaluate our approach on the KITTI odometry dataset and have manually generated ground truth for object class segmentation. Our qualitative evaluation is performed on various sequences of the dataset and we also quantify our results on a representative subset.

Linear SVMs are efficient in both training and testing, however the data in real applications is rarely linearly separable. Non-linear kernel SVMs are too computationally intensive for applications with large-scale data sets. Recently locally linear classifiers have gained popularity due to their efficiency whilst remaining competitive with kernel methods. The vanilla nearest neighbor algorithm is one of the simplest locally linear classifiers, but it lacks robustness due to the noise often present in real-world data. In this paper, we introduce a novel local classifier, Parametric Nearest Neighbor (P-NN) and its extension Ensemble of P-NN (EP-NN). We parameterize the nearest neighbor algorithm based on the minimum weighted squared Euclidean distances between the data points and the prototypes, where a prototype is represented by a locally linear combination of some data points. Meanwhile, our method attempts to jointly learn both the prototypes and the classifier parameters discriminatively via max-margin. This makes our classifiers suitable to approximate the classification decision boundaries locally based on nonlinear functions. During testing, the computational complexity of both classifiers is linear in the product of the dimension of data and the number of prototypes. Our classification results on MNIST, USPS, LETTER, and Chars 74K are comparable and in some cases are better than many other methods such as the state-of-the-art locally linear classifiers.

This paper describes a method for producing a semantic map from multi-view street-level imagery. We define a semantic map as an overhead, or bird's eye view of a region with associated semantic object labels, such as car, road and pavement. We formulate the problem using two conditional random fields. The first is used to model the semantic image segmentation of the street view imagery treating each image independently. The outputs of this stage are then aggregated over many images to form the input for our semantic map that is a second random field defined over a ground plane. Each image is related by a simple, yet effective, geometrical function that back projects a region from the street view image into the overhead ground plane map. We introduce, and make publicly available, a new dataset created from real world data. Our qualitative evaluation is performed on this data consisting of a 14.8 km track, and we also quantify our results on a representative subset.

This work is supported by EPSRC research grants, HMGCC, TUBITAK researcher exchange grant, the IST Programme of the European Community, under the PASCAL2 Network of Excellence, IST-2007-216886.