Image aesthetics assessment (IAA) has been traditionally addressed as a supervised learning problem, where the goal is to accurately predict information related to user opinions, such as the mean opinion score, image ratings, or a binary quality label, usually crafted by using a mean score threshold to label images as highly or lowly aesthetic. Supervised approaches fail to take into account the subjectiveness of this problem, as the idea of aesthetic pleasantness varies among different people and different cultures, thus making the labels extremely noisy. However, the existence of worldwide photographic contests, exhibitions and masters implies that, to a reasonable degree, there is a broader consensus about the quality of very high-quality images and photographs. Furthermore, labelling image data for IAA is a difficult process, as a large amount of non-trivial aesthetic judgements are required for obtaining a large-scale IAA dataset. Therefore, in this work we analyse the potential of positive-unlabelled techniques for solving IAA. We propose techniques for building PU datasets from traditional IAA datasets and from available reference datasets of high-quality images, and test several well-known PU algorithms on these. Our results highlight the potential of PU approaches for IAA, as we obtain results close to the state-of-the-art with much smaller sets of labelled data: in experiments with only 5% of labelled in AVA, we reach accuracy levels only 0.03 points below NIMA, and we reach competent balanced accuracy levels in settings with a very limited amount of labelled data and with very simple models. • We study solving image aesthetic assessment as a positive unlabelled problem. • We make positive unlabelled datasets from known image aesthetic assessment datasets. • We get a balanced accuracy 0.03 points below NIMA with 5% of labelled images in AVA.

Aesthetic assessment evaluates the quality of a given image using subjective annotations, commonly user ratings, as a knowledge base. Rating complexity is usually relaxed in state-of-the-art works by employing a binary high/low quality label computed from the mean value of rating votes. Nevertheless, this approach introduces uncertainty to average-quality images, which may affect the performance of machine learning models trained from annotated data. In this work, we present a novel approach to aesthetic assessment based on redefining the rating-based groundtruths present in most datasets. Our intent is twofold: to reduce the rating uncertainty and to automatically group them into clusters reflecting high and low quality patterns, thus avoiding an arbitrary threshold like 5 in 1–10 ratings. The experimentation uses the well-known AVA dataset, which consists of more than 255,000 images, and we train several CNN models to test our new groundtruths against the baseline ones. The results show that our approach achieves significant performance gains, between 3% and 9% more balanced accuracy than the baseline groundtruths.

Abstract Many of the research problems in robot vision involve the detection of keypoints, areas with salient information in the input images and the generation of local descriptors, that encode relevant information for such keypoints. Computer vision solutions have recently relied on Deep Learning techniques, which make extensive use of the computational capabilities available. In autonomous robots, these capabilities are usually limited and, consequently, images cannot be processed adequately. For this reason, some robot vision tasks still benefit from a more classic approach based on keypoint detectors and local descriptors. In 2D images, the use of binary representations for visual tasks has shown that, with lower computational requirements, they can obtain a performance comparable to classic real-value techniques. However, these achievements have not been fully translated to 3D images, where research is mainly focused on real-value approaches. Thus, in this paper, we propose a keypoint detector and local descriptor based on 3D binary patterns. The experimentation demonstrates that our proposal is competitive against state-of-the-art techniques, while its processing can be performed more efficiently.

One of the most important tasks in computer vision systems is the description of the local neighborhood around interest points. In RGB-D images, this task is usually performed by computing descriptors encoded as real-value vectors. The use of binary descriptors, like BRISK, BRIEF or ORB, has proven to be adequate to address this task accurately and efficiently. In this paper, we propose a novel binary pattern that encodes the shape around a given point in a RGB-D image with invariance to rotation and scale. This descriptor is contrasted to well-known state-of-the-art 3D descriptors, namely FPFH, Spin Images, SHOT, PFHRGB and CSHOT in order to test its actual performance on the RGB-D Objects dataset. The experiments performed show that the proposed binary pattern descriptor equals and even outperforms state-of-the-art 3D descriptors with a higher computational efficiency.

There exist a wide number of works in the literature related to new systems devoted to manage thermal control in buildings. Commonly, their evaluation is performed by using simulation of users and environmental conditions. Thus, in this work we choose a successful thermal-comfort system, formerly evaluated with simulations, and evaluate it by using data from project ASHRAE RP-884, which provides logs of real data coming from different buildings, in a wide variety of climates, and occupied by people with different thermal preferences. From these logs, we propose a pre-processing and evaluation methodology in order to achieve more realistic evaluations.

The ability of building robust semantic space representations of environments is crucial for the development of truly autonomous robots. This task, inherently connected with cognition, is traditionally achieved by training the robot with a supervised learning phase. We argue that the design of robust and autonomous systems would greatly benefit from adopting a semi-supervised online learning approach. Indeed, the support of open-ended, lifelong learning is fundamental in order to cope with the dazzling variability of the real world, and online learning provides precisely this kind of ability. Here we focus on the robot place recognition problem, and we present an online place classification algorithm that is able to detect gap in its own knowledge based on a confidence measure. For every incoming new image frame, the method is able to decide if (a) it is a known room with a familiar appearance, (b) it is a known room with a challenging appearance, or (c) it is a new, unknown room. Experiments on a subset of the challenging COLD database show the promise of our approach.

This paper presents a new approach for the classical Markov localization method for mobile robots by using image quality evaluation. Machine learning techniques have been used to obtain the quality of the images. This quality value is used to select the best information source, between odometry and sensor information. Real experiments in different scenarios of the Robocup standard platform league are also presented.