A study of observation scales based on Felzenswalb-Huttenlocher dissimilarity measure for hierarchical segmentation

International audienceHierarchical image segmentation provides a region-oriented scale-space, i.e., a set of image segmentations at different detail levels in which the segmentations at finer levels are nested with respect to those at coarser levels. Guimarães et al. proposed a hierarchical graph based image segmentation (HGB) method based on the Felzenszwalb-Huttenlocher dissimilarity. This HGB method computes, for each edge of a graph, the minimum scale in a hierarchy at which two regions linked by this edge should merge according to the dissimilarity. In order to generalize this method, we first propose an algorithm to compute the intervals which contain all the observation scales at which the associated regions should merge. Then, following the current trend in mathematical morphology to study criteria which are not increasing on a hierarchy, we present various strategies to select a significant observation scale in these intervals. We use the BSDS dataset to assess our observation scale selection methods. The experiments show that some of these strategies lead to better segmentation results than the ones obtained with the original HGB method

People Efficiently Explore the Solution Space of the Computationally Intractable Traveling Salesman Problem to Find Near-Optimal Tours

Humans need to solve computationally intractable problems such as visual search, categorization, and simultaneous learning and acting, yet an increasing body of evidence suggests that their solutions to instantiations of these problems are near optimal. Computational complexity advances an explanation to this apparent paradox: (1) only a small portion of instances of such problems are actually hard, and (2) successful heuristics exploit structural properties of the typical instance to selectively improve parts that are likely to be sub-optimal. We hypothesize that these two ideas largely account for the good performance of humans on computationally hard problems. We tested part of this hypothesis by studying the solutions of 28 participants to 28 instances of the Euclidean Traveling Salesman Problem (TSP). Participants were provided feedback on the cost of their solutions and were allowed unlimited solution attempts (trials). We found a significant improvement between the first and last trials and that solutions are significantly different from random tours that follow the convex hull and do not have self-crossings. More importantly, we found that participants modified their current better solutions in such a way that edges belonging to the optimal solution (“good” edges) were significantly more likely to stay than other edges (“bad” edges), a hallmark of structural exploitation. We found, however, that more trials harmed the participants' ability to tell good from bad edges, suggesting that after too many trials the participants “ran out of ideas.” In sum, we provide the first demonstration of significant performance improvement on the TSP under repetition and feedback and evidence that human problem-solving may exploit the structure of hard problems paralleling behavior of state-of-the-art heuristics

Effective Programming of Combinatorial Maps using COMA- A C++ Framework for Combinatorial Maps

Combinatorial maps and pyramids have been studied in great detail in the past, and it has been shown that this concept is advantageous for many applications in the field of image processing and pattern recognition by providing means to store information of the topological relations of the represented data. In the course of these studies, the properties of combinatorial maps have been investigated using different sets of permutations, different operations and different algorithms. In each case new software had to be created in order to conduct experiments, as the existing programs were designed to work only for a specific model. Due to the complexity of combinatorial maps, the implementation of such a software is a time and resource intensive task. Thus these programming efforts were often responsible for delaying the presentation of new results in the past. This paper presents COMA- a C++ framework for combinatorial maps- that has been created during recent studies of combinatorial maps, motivated by this problem. Using an object oriented approach, COMA was specifically designed to allow an efficient and quick integration of changes to the model of combinatorial maps used, as well as the implementation of new algorithms. As a consequence COMA significantly reduces the amount of time needed to set up new experiments.

On the computational power and complexity of spiking neural networks

Item does not contain fulltextThe last decade has seen the rise of neuromorphic architectures based on artificial spiking neural networks, such as the SpiNNaker, TrueNorth, and Loihi systems. The massive parallelism and co-locating of computation and memory in these architectures potentially allows for an energy usage that is orders of magnitude lower compared to traditional Von Neumann architectures. However, to date a comparison with more traditional computational architectures (particularly with respect to energy usage) is hampered by the lack of a formal machine model and a computational complexity theory for neuromorphic computation. In this paper we take the first steps towards such a theory. We introduce spiking neural networks as a machine model where - in contrast to the familiar Turing machine - information and the manipulation thereof are co-located in the machine. We introduce canonical problems, define hierarchies of complexity classes and provide some first completeness results.NICE '20: Neuro-inspired Computational Elements Workshop (Heidelberg, Germany, March, 2020

Segmentation Graph Hierarchies

Abstract. The region’s internal properties (color, texture,...) help to identify them and their external relations (adjacency, inclusion,...) are used to build groups of regions having a particular consistent meaning in a more abstract context. Low-level cue image segmentation in a bottom-up way, cannot and should not produce a complete final “good” segmentation. We present a hierarchical partitioning of images using a pairwise similarity function on a graph-based representation of an image. The aim of this paper is to build a minimum weight spanning tree (MST) of an image in order to find region borders quickly in a bottom-up ’stimulus-driven ’ way based on local differences in a specific feature.

Hierarchy of Partitions with Dual Graph Contraction

We present a hierarchical partitioning of images using a pairwise similarity function on a graph-based representation of an image