One-class classifiers based on entropic spanning graphs

One-class classifiers offer valuable tools to assess the presence of outliers in data. In this paper, we propose a design methodology for one-class classifiers based on entropic spanning graphs. Our approach takes into account the possibility to process also non-numeric data by means of an embedding procedure. The spanning graph is learned on the embedded input data and the outcoming partition of vertices defines the classifier. The final partition is derived by exploiting a criterion based on mutual information minimization. Here, we compute the mutual information by using a convenient formulation provided in terms of the $\alpha$-Jensen difference. Once training is completed, in order to associate a confidence level with the classifier decision, a graph-based fuzzy model is constructed. The fuzzification process is based only on topological information of the vertices of the entropic spanning graph. As such, the proposed one-class classifier is suitable also for data characterized by complex geometric structures. We provide experiments on well-known benchmarks containing both feature vectors and labeled graphs. In addition, we apply the method to the protein solubility recognition problem by considering several representations for the input samples. Experimental results demonstrate the effectiveness and versatility of the proposed method with respect to other state-of-the-art approaches.Comment: Extended and revised version of the paper "One-Class Classification Through Mutual Information Minimization" presented at the 2016 IEEE IJCNN, Vancouver, Canad

Robust and Fast 3D Scan Alignment using Mutual Information

This paper presents a mutual information (MI) based algorithm for the estimation of full 6-degree-of-freedom (DOF) rigid body transformation between two overlapping point clouds. We first divide the scene into a 3D voxel grid and define simple to compute features for each voxel in the scan. The two scans that need to be aligned are considered as a collection of these features and the MI between these voxelized features is maximized to obtain the correct alignment of scans. We have implemented our method with various simple point cloud features (such as number of points in voxel, variance of z-height in voxel) and compared the performance of the proposed method with existing point-to-point and point-to- distribution registration methods. We show that our approach has an efficient and fast parallel implementation on GPU, and evaluate the robustness and speed of the proposed algorithm on two real-world datasets which have variety of dynamic scenes from different environments

Hashmod: A Hashing Method for Scalable 3D Object Detection

We present a scalable method for detecting objects and estimating their 3D poses in RGB-D data. To this end, we rely on an efficient representation of object views and employ hashing techniques to match these views against the input frame in a scalable way. While a similar approach already exists for 2D detection, we show how to extend it to estimate the 3D pose of the detected objects. In particular, we explore different hashing strategies and identify the one which is more suitable to our problem. We show empirically that the complexity of our method is sublinear with the number of objects and we enable detection and pose estimation of many 3D objects with high accuracy while outperforming the state-of-the-art in terms of runtime.Comment: BMVC 201

Iterative estimation of mutual information with error bounds

Mutual Information (MI) is an established measure for linear and nonlinear dependencies between two variables. Estimating MI is nontrivial and requires notable computation power for high estimation quality. While some estimation techniques allow trading result quality for lower runtimes, this tradeoff is fixed per task and cannot be adjusted. If the available time is unknown in advance or is overestimated, one may need to abort the estimation without any result. Conversely, when there are several estimation tasks, and one wants to budget computation time between them, there currently is no efficient way to adjust it dynamically based on certain targets, e.g., high MI values or MI values close to a constant. In this article, we present an iterative estimator of MI. Our method offers an estimate with low quality near-instantly and improves this estimate in fine grained steps with more computation time. The estimate also converges towards the result of a conventional estimator. We prove that the time complexity for this convergence is only slightly slower than non-iterative estimation. Additionally, with each step our estimator also tightens statistical guarantees regarding the convergence result, i.e., confidence intervals, progressively. These also serve as quality indicators for early estimates and allow to reliably discern between attribute pairs with weak and strong dependencies. Our experiments show that these guarantees can also be used to execute threshold queries faster compared to non-iterative estimation

SPOT: Sliced Partial Optimal Transport

International audienceOptimal transport research has surged in the last decade with wide applications in computer graphics. In most cases, however, it has focused on the special case of the so-called ``balanced'' optimal transport problem, that is, the problem of optimally matching positive measures of equal total mass. While this approach is suitable for handling probability distributions as their total mass is always equal to one, it precludes other applications manipulating disparate measures.Our paper proposes a fast approach to the optimal transport of constant distributions supported on point sets of different cardinality via one-dimensional slices. This leads to one-dimensional partial assignment problems akin to alignment problems encountered in genomics or text comparison. Contrary to one-dimensional balanced optimal transport that leads to a trivial linear-time algorithm, such partial optimal transport, even in 1-d, has not seen any closed-form solution nor very efficient algorithms to date.We provide the first efficient 1-d partial optimal transport solver. Along with a quasilinear time problem decomposition algorithm, it solves 1-d assignment problems consisting of up to millions of Dirac distributions within fractions of a second in parallel.We handle higher dimensional problems via a slicing approach, and further extend the popular iterative closest point algorithm using optimal transport -- an algorithm we call Fast Iterative Sliced Transport. We illustrate our method on computer graphics applications such a color transfer and point cloud registration