Search results for "Image processing"
showing 10 items of 3285 documents
How does serendipity affect diversity in recommender systems? A serendipity-oriented greedy algorithm
2018
Most recommender systems suggest items that are popular among all users and similar to items a user usually consumes. As a result, the user receives recommendations that she/he is already familiar with or would find anyway, leading to low satisfaction. To overcome this problem, a recommender system should suggest novel, relevant and unexpected i.e., serendipitous items. In this paper, we propose a serendipity-oriented, reranking algorithm called a serendipity-oriented greedy (SOG) algorithm, which improves serendipity of recommendations through feature diversification and helps overcome the overspecialization problem. To evaluate our algorithm, we employed the only publicly available datase…
Ranking-Oriented Collaborative Filtering: A Listwise Approach
2016
Collaborative filtering (CF) is one of the most effective techniques in recommender systems, which can be either rating oriented or ranking oriented. Ranking-oriented CF algorithms demonstrated significant performance gains in terms of ranking accuracy, being able to estimate a precise preference ranking of items for each user rather than the absolute ratings (as rating-oriented CF algorithms do). Conventional memory-based ranking-oriented CF can be referred to as pairwise algorithms. They represent each user as a set of preferences on each pair of items for similarity calculations and predictions. In this study, we propose ListCF, a novel listwise CF paradigm that seeks improvement in bot…
The effect of automated taxa identification errors on biological indices
2017
In benthic macroinvertebrate biomonitoring systems, the target is to determine the status of ecosystems based on several biological indices. To increase cost-efficiency, computer-based taxa identification for image data has recently been developed. Taxa identification errors can, however, have strong effects on the indices and thus on the determination of the ecological status. In order to shift the biomonitoring process towards automated expert systems, we need a clear understanding on the bias caused by automation. In this paper, we examine eleven classification methods in the case of macroinvertebrate image data and show how their classification errors propagate into different biological…
Topology Inference and Signal Representation Using Dictionary Learning
2019
This paper presents a Joint Graph Learning and Signal Representation algorithm, called JGLSR, for simultaneous topology learning and graph signal representation via a learned over-complete dictionary. The proposed algorithm alternates between three main steps: sparse coding, dictionary learning, and graph topology inference. We introduce the “transformed graph” which can be considered as a projected graph in the transform domain spanned by the dictionary atoms. Simulation results via synthetic and real data show that the proposed approach has a higher performance when compared to the well-known algorithms for joint undirected graph topology inference and signal representation, when there is…
SCCF Parameter and Similarity Measure Optimization and Evaluation
2019
Neighborhood-based Collaborative Filtering (CF) is one of the most successful and widely used recommendation approaches; however, it suffers from major flaws especially under sparse environments. Traditional similarity measures used by neighborhood-based CF to find similar users or items are not suitable in sparse datasets. Sparse Subspace Clustering and common liking rate in CF (SCCF), a recently published research, proposed a tunable similarity measure oriented towards sparse datasets; however, its performance can be maximized and requires further analysis and investigation. In this paper, we propose and evaluate the performance of a new tuning mechanism, using the Mean Absolute Error (MA…
Path Planning for Perception-Driven Obstacle-Aided Snake Robot Locomotion
2020
Development of snake robots have been motivated by the ability of snakes to move efficiently in unstructured and cluttered environments. A snake robot has the potential to utilise obstacles for generating locomotion, in contrast to wheeled robots which are unable to move efficiently in rough terrain. In this paper, we propose a local path planning algorithm for snake robots based on obstacle-aided locomotion (OAL). An essential feature in OAL is to determine suitable push-points in the environment that the snake robot can use for locomotion. The proposed method is based on a set of criteria for evaluating a path, and is a novel contribution of this paper. We focus on local path planning and…
Image-based 3D reconstruction using traditional and UAV datasets for analysis of road pavement distress
2018
Abstract On local and urban networks, the enduring issue of scarce resources for Maintenance, Rehabilitation, and Reconstruction strategies (MR&R) has led, in many cases, to using unadjusted or poor techniques for road pavement distress detection and analysis, yielding ineffective or even counterproductive results. Therefore, it is necessary to have tools that can carry out quick, reliable and low-cost assessment surveys. This paper aims at validating the use of innovative and low-cost technologies for road pavement analysis, assessing their potentialities for improving the automation and reliability of distress detection. A Structure from Motion (SfM) technique is analyzed at different alt…
Pini Language and PiniTree Ontology Editor: Annotation and Verbalisation for Atomised Journalism
2020
We present a new ontology language Pini and the PiniTree ontology editor supporting it. Despite Pini language bearing lot of similarities with RDF, UML class diagrams, Property Graphs and their frontends like Google Knowledge Graph and Protege, it is a more expressive language enabling FrameNet-style natural language annotation for Atomised journalism use case.
Combining Supervised and Unsupervised Learning to Discover Emotional Classes
2017
Most previous work in emotion recognition has fixed the available classes in advance, and attempted to classify samples into one of these classes using a supervised learning approach. In this paper, we present preliminary work on combining supervised and unsupervised learning to discover potential latent classes which were not initially considered. To illustrate the potential of this hybrid approach, we have used a Self-Organizing Map (SOM) to organize a large number of Electroencephalogram (EEG) signals from subjects watching videos, according to their internal structure. Results suggest that a more useful labelling scheme could be produced by analysing the resulting topology in relation t…
Stereopsis assessment at multiple distances with an iPad application
2017
[EN] We present a new application for iPad for screening stereopsis at multiple distances that allows testing up to ten levels of stereoacuity at each distance. Our approach is based on a random dot stereogram viewable with anaglyph spectacles. Sixty-five subjects with no ocular diseases, wearing their habitual correction were measured at 3 m and 0.5 m. Results were compared with a standard stereoscopic test (TNO). We found not statistically significant differences between both tests, but our method achieved higher reproducibility. Applications in visual screening programs and to design and use of 3D displays, are suggested. (C) 2017 Elsevier B.V. All rights reserved.