Complex Network Construction for Interactive Image Segmentation using Particle Competition and Cooperation: A New Approach

In the interactive image segmentation task, the Particle Competition and Cooperation (PCC) model is fed with a complex network, which is built from the input image. In the network construction phase, a weight vector is needed to define the importance of each element in the feature set, which consists of color and location information of the corresponding pixels, thus demanding a specialist's intervention. The present paper proposes the elimination of the weight vector through modifications in the network construction phase. The proposed model and the reference model, without the use of a weight vector, were compared using 151 images extracted from the Grabcut dataset, the PASCAL VOC dataset and the Alpha matting dataset. Each model was applied 30 times to each image to obtain an error average. These simulations resulted in an error rate of only 0.49\% when classifying pixels with the proposed model while the reference model had an error rate of 3.14\%. The proposed method also presented less error variation in the diversity of the evaluated images, when compared to the reference model.Comment: The 20th International Conference on Computational Science and its Applications (ICCSA2020

Building Networks for Image Segmentation using Particle Competition and Cooperation

Particle competition and cooperation (PCC) is a graph-based semi-supervised learning approach. When PCC is applied to interactive image segmentation tasks, pixels are converted into network nodes, and each node is connected to its k-nearest neighbors, according to the distance between a set of features extracted from the image. Building a proper network to feed PCC is crucial to achieve good segmentation results. However, some features may be more important than others to identify the segments, depending on the characteristics of the image to be segmented. In this paper, an index to evaluate candidate networks is proposed. Thus, building the network becomes a problem of optimizing some feature weights based on the proposed index. Computer simulations are performed on some real-world images from the Microsoft GrabCut database, and the segmentation results related in this paper show the effectiveness of the proposed method

Visually Impaired Aid using Convolutional Neural Networks, Transfer Learning, and Particle Competition and Cooperation

Navigation and mobility are some of the major problems faced by visually impaired people in their daily lives. Advances in computer vision led to the proposal of some navigation systems. However, most of them require expensive and/or heavy hardware. In this paper we propose the use of convolutional neural networks (CNN), transfer learning, and semi-supervised learning (SSL) to build a framework aimed at the visually impaired aid. It has low computational costs and, therefore, may be implemented on current smartphones, without relying on any additional equipment. The smartphone camera can be used to automatically take pictures of the path ahead. Then, they will be immediately classified, providing almost instantaneous feedback to the user. We also propose a dataset to train the classifiers, including indoor and outdoor situations with different types of light, floor, and obstacles. Many different CNN architectures are evaluated as feature extractors and classifiers, by fine-tuning weights pre-trained on a much larger dataset. The graph-based SSL method, known as particle competition and cooperation, is also used for classification, allowing feedback from the user to be incorporated without retraining the underlying network. 92\% and 80\% classification accuracy is achieved in the proposed dataset in the best supervised and SSL scenarios, respectively.Comment: BREVE, Fabricio Aparecido; FISCHER, Carlos Norberto. Visually Impaired Aid using Convolutional Neural Networks, Transfer Learning, and Particle Competition and Cooperation In: 2020 International Joint Conference on Neural Networks (IJCNN 2020), 2020, Glasgow, UK. Proceedings of 2020 International Joint Conference on Neural Networks (IJCNN 2020), 2020. (accepted for publication

Simple Interactive Image Segmentation using Label Propagation through kNN graphs

Many interactive image segmentation techniques are based on semi-supervised learning. The user may label some pixels from each object and the SSL algorithm will propagate the labels from the labeled to the unlabeled pixels, finding object boundaries. This paper proposes a new SSL graph-based interactive image segmentation approach, using undirected and unweighted kNN graphs, from which the unlabeled nodes receive contributions from other nodes (either labeled or unlabeled). It is simpler than many other techniques, but it still achieves significant classification accuracy in the image segmentation task. Computer simulations are performed using some real-world images, extracted from the Microsoft GrabCut dataset. The segmentation results show the effectiveness of the proposed approach

Interactive Image Segmentation using Label Propagation through Complex Networks

Interactive image segmentation is a topic of many studies in image processing. In a conventional approach, a user marks some pixels of the object(s) of interest and background, and an algorithm propagates these labels to the rest of the image. This paper presents a new graph-based method for interactive segmentation with two stages. In the first stage, nodes representing pixels are connected to their $k$-nearest neighbors to build a complex network with the small-world property to propagate the labels quickly. In the second stage, a regular network in a grid format is used to refine the segmentation on the object borders. Despite its simplicity, the proposed method can perform the task with high accuracy. Computer simulations are performed using some real-world images to show its effectiveness in both two-classes and multi-classes problems. It is also applied to all the images from the Microsoft GrabCut dataset for comparison, and the segmentation accuracy is comparable to those achieved by some state-of-the-art methods, while it is faster than them. In particular, it outperforms some recent approaches when the user input is composed only by a few "scribbles" draw over the objects. Its computational complexity is only linear on the image size at the best-case scenario and linearithmic in the worst case.Comment: Paper accepted for publication in Expert Systems With Application