Weighted Point Cloud Augmentation for Neural Network Training Data Class-Imbalance

Recent developments in the field of deep learning for 3D data have demonstrated promising potential for end-to-end learning directly from point clouds. However, many real-world point clouds contain a large class im-balance due to the natural class im-balance observed in nature. For example, a 3D scan of an urban environment will consist mostly of road and facade, whereas other objects such as poles will be under-represented. In this paper we address this issue by employing a weighted augmentation to increase classes that contain fewer points. By mitigating the class im-balance present in the data we demonstrate that a standard PointNet++ deep neural network can achieve higher performance at inference on validation data. This was observed as an increase of F1 score of 19% and 25% on two test benchmark datasets; ScanNet and Semantic3D respectively where no class im-balance pre-processing had been performed. Our networks performed better on both highly-represented and under-represented classes, which indicates that the network is learning more robust and meaningful features when the loss function is not overly exposed to only a few classes.Comment: 7 pages, 6 figures, submitted for ISPRS Geospatial Week conference 201

Radar-based Feature Design and Multiclass Classification for Road User Recognition

The classification of individual traffic participants is a complex task, especially for challenging scenarios with multiple road users or under bad weather conditions. Radar sensors provide an - with respect to well established camera systems - orthogonal way of measuring such scenes. In order to gain accurate classification results, 50 different features are extracted from the measurement data and tested on their performance. From these features a suitable subset is chosen and passed to random forest and long short-term memory (LSTM) classifiers to obtain class predictions for the radar input. Moreover, it is shown why data imbalance is an inherent problem in automotive radar classification when the dataset is not sufficiently large. To overcome this issue, classifier binarization is used among other techniques in order to better account for underrepresented classes. A new method to couple the resulting probabilities is proposed and compared to others with great success. Final results show substantial improvements when compared to ordinary multiclass classificationComment: 8 pages, 6 figure

Ensembles of probability estimation trees for customer churn prediction

Customer churn prediction is one of the most, important elements tents of a company's Customer Relationship Management, (CRM) strategy In tins study, two strategies are investigated to increase the lift. performance of ensemble classification models, i.e (1) using probability estimation trees (PETs) instead of standard decision trees as base classifiers; and (n) implementing alternative fusion rules based on lift weights lot the combination of ensemble member's outputs Experiments ale conducted lot font popular ensemble strategics on five real-life chin n data sets In general, the results demonstrate how lift performance can be substantially improved by using alternative base classifiers and fusion tides However: the effect vanes lot the (Idol cut ensemble strategies lit particular, the results indicate an increase of lift performance of (1) Bagging by implementing C4 4 base classifiets. (n) the Random Subspace Method (RSM) by using lift-weighted fusion rules, and (in) AdaBoost, by implementing both

Recognizing the presence of hidden visual markers in digital images

As the promise of Virtual and Augmented Reality (VR and AR) becomes more realistic, an interesting aspect of our enhanced living environment includes the availability — indeed the potential ubiquity — of scannable markers. Such markers could represent an initial step into the AR and VR worlds. In this paper, we address the important question of how to recognise the presence of visual markers in freeform digital photos. We use a particularly challenging marker format that is only minimally constrained in structure, called Artcodes. Artcodes are a type of topological marker system enabling people, by following very simple drawing rules, to design markers that are both aesthetically beautiful and machine readable. Artcodes can be used to decorate the surface of any objects, and yet can also contain a hidden digital meaning. Like some other more commonly used markers (such as Barcodes, QR codes), it is possible to use codes to link physical objects to digital data, augmenting everyday objects. Obviously, in order to trigger the behaviour of scanning and further decoding of such codes, it is first necessary for devices to be aware of the presence of Artcodes in the image. Although considerable literature exists related to the detection of rigidly formatted structures and geometrical feature descriptors such as Harris, SIFT, and SURF, these approaches are not sufficient for describing freeform topological structures, such as Artcode images. In this paper, we propose a new topological feature descriptor that can be used in the detection of freeform topological markers, including Artcodes. This feature descriptor is called a Shape of Orientation Histogram (SOH). We construct this SOH feature vector by quantifying the level of symmetry and smoothness of the orientation histogram, and then use a Random Forest machine learning approach to classify images that contain Artcodes using the new feature vector. This system represents a potential first step for an eventual mobile device application that would detect where in an image such an unconstrained code appears. We also explain how the system handles imbalanced datasets — important for rare, handcrafted codes such as Artcodes — and how it is evaluated. Our experimental evaluation shows good performance of the proposed classification model in the detection of Artcodes: obtaining an overall accuracy of approx. 0.83, F2 measure 0.83, MCC 0.68, AUC-ROC 0.93, and AUC-PR 0.91

A Nonparametric Ensemble Binary Classifier and its Statistical Properties

In this work, we propose an ensemble of classification trees (CT) and artificial neural networks (ANN). Several statistical properties including universal consistency and upper bound of an important parameter of the proposed classifier are shown. Numerical evidence is also provided using various real life data sets to assess the performance of the model. Our proposed nonparametric ensemble classifier doesn't suffer from the `curse of dimensionality' and can be used in a wide variety of feature selection cum classification problems. Performance of the proposed model is quite better when compared to many other state-of-the-art models used for similar situations

Estudio de métodos de construcción de ensembles de clasificadores y aplicaciones

La inteligencia artificial se dedica a la creación de sistemas informáticos con un comportamiento inteligente. Dentro de este área el aprendizaje computacional estudia la creación de sistemas que aprenden por sí mismos. Un tipo de aprendizaje computacional es el aprendizaje supervisado, en el cual, se le proporcionan al sistema tanto las entradas como la salida esperada y el sistema aprende a partir de estos datos. Un sistema de este tipo se denomina clasificador. En ocasiones ocurre, que en el conjunto de ejemplos que utiliza el sistema para aprender, el número de ejemplos de un tipo es mucho mayor que el número de ejemplos de otro tipo. Cuando esto ocurre se habla de conjuntos desequilibrados. La combinación de varios clasificadores es lo que se denomina "ensemble", y a menudo ofrece mejores resultados que cualquiera de los miembros que lo forman. Una de las claves para el buen funcionamiento de los ensembles es la diversidad. Esta tesis, se centra en el desarrollo de nuevos algoritmos de construcción de ensembles, centrados en técnicas de incremento de la diversidad y en los problemas desequilibrados. Adicionalmente, se aplican estas técnicas a la solución de varias problemas industriales.Ministerio de Economía y Competitividad, proyecto TIN-2011-2404