Search results for "Image processing"
showing 10 items of 3285 documents
User session level diverse reranking of search results
2018
Most Web search diversity approaches can be categorized as Document Level Diversification (DocLD), Topic Level Diversification (TopicLD) or Term Level Diversification (TermLD). DocLD selects the relevant documents with minimal content overlap to each other. It does not take the coverage of query subtopics into account. TopicLD solves this by modeling query subtopics explicitly. However, the automatic mining of query subtopics is difficult. TermLD tries to cover as many query topic terms as possible, which reduces the task of finding a query's subtopics into finding a set of representative topic terms. In this paper, we propose a novel User Session Level Diversification (UserLD) approach bas…
A New Augmented Lagrangian Approach for $L^1$-mean Curvature Image Denoising
2015
Variational methods are commonly used to solve noise removal problems. In this paper, we present an augmented Lagrangian-based approach that uses a discrete form of the L1-norm of the mean curvature of the graph of the image as a regularizer, discretization being achieved via a finite element method. When a particular alternating direction method of multipliers is applied to the solution of the resulting saddle-point problem, this solution reduces to an iterative sequential solution of four subproblems. These subproblems are solved using Newton’s method, the conjugate gradient method, and a partial solution variant of the cyclic reduction method. The approach considered here differs from ex…
Exploiting ongoing EEG with multilinear partial least squares during free-listening to music
2016
During real-world experiences, determining the stimulus-relevant brain activity is excitingly attractive and is very challenging, particularly in electroencephalography. Here, spectrograms of ongoing electroencephalogram (EEG) of one participant constructed a third-order tensor with three factors of time, frequency and space; and the stimulus data consisting of acoustical features derived from the naturalistic and continuous music formulated a matrix with two factors of time and the number of features. Thus, the multilinear partial least squares (PLS) conforming to the canonical polyadic (CP) model was performed on the tensor and the matrix for decomposing the ongoing EEG. Consequently, we …
BioImageXD: an open, general-purpose and high-throughput image-processing platform
2012
BioImageXD puts open-source computer science tools for three-dimensional visualization and analysis into the hands of all researchers, through a user-friendly graphical interface tuned to the needs of biologists. BioImageXD has no restrictive licenses or undisclosed algorithms and enables publication of precise, reproducible and modifiable workflows. It allows simple construction of processing pipelines and should enable biologists to perform challenging analyses of complex processes. We demonstrate its performance in a study of integrin clustering in response to selected inhibitors.
A Serendipity-Oriented Greedy Algorithm for Recommendations
2017
Most recommender systems suggest items to a user that are popular among all users and similar to items the user usually consumes. As a result, a 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 algorithm, which improves serendipity through feature diversification and helps overcome the overspecialization problem. To evaluate our algorithm and compare it with others, we employ a serendipity metric that captures each component of serendipity, unlike the most …
A two-step, user-centered approach to personalized tourist recommendations
2017
Geo-localized, mobile applications can simplify a tourist visit, making the relevant Point of Interests more easily and promptly discernible to users. At the same time, such solutions must avoid creating unfitting or rigid user profiles that impoverish the users' options instead of refining them. Currently, user profiles in recommender systems rely on dimensions whose relevance to the user is more often presumed than empirically defined. To avoid this drawback, we build our recommendation system in a two-step process, where profile parameters are evaluated preliminarily and separately from the recommendations themselves. We describe this two-step evaluation process including an initial surv…
Semi-automatic literature mapping of participatory design studies 2006--2016
2018
The paper presents a process of semi-automatic literature mapping of a comprehensive set of participatory design studies between 2006--2016. The data of 2939 abstracts were collected from 14 academic search engines and databases. With the presented method, we were able to identify six education-related clusters of PD articles. Furthermore, we point out that the identified clusters cover the majority of education-related words in the whole data. This is the first attempt to systematically map the participatory design literature. We argue that by continuing our work, we can help to perceive a coherent structure in the body of PD research.
Social Collaborative Viewpoint Regression with Explainable Recommendations
2017
A recommendation is called explainable if it not only predicts a numerical rating for an item, but also generates explanations for users' preferences. Most existing methods for explainable recommendation apply topic models to analyze user reviews to provide descriptions along with the recommendations they produce. So far, such methods have neglected user opinions and influences from social relations as a source of information for recommendations, even though these are known to improve the rating prediction. In this paper, we propose a latent variable model, called social collaborative viewpoint regression (sCVR), for predicting item ratings based on user opinions and social relations. To th…
Turing's error-revised
2016
Many important lines of argumentation have been presented during the last decades claiming that machines cannot think like people. Yet, it has been possible to construct devices and information systems, which replace people in tasks which have previously been occupied by people as the tasks require intelligence. The long and versatile discourse over, what machine intelligence is, suggests that there is something unclear in the foundations of the discourse itself. Therefore, we critically studied the foundations of used theory languages. By looking critically some of the main arguments of machine thinking, one can find unifying factors. Most of them are based on the fact that computers canno…
Biased graph walks for RDF graph embeddings
2017
Knowledge Graphs have been recognized as a valuable source for background information in many data mining, information retrieval, natural language processing, and knowledge extraction tasks. However, obtaining a suitable feature vector representation from RDF graphs is a challenging task. In this paper, we extend the RDF2Vec approach, which leverages language modeling techniques for unsupervised feature extraction from sequences of entities. We generate sequences by exploiting local information from graph substructures, harvested by graph walks, and learn latent numerical representations of entities in RDF graphs. We extend the way we compute feature vector representations by comparing twel…