The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
This paper proposes efficient and powerful deep networks for action prediction from partially observed videos containing temporally incomplete action executions. Different from after-the-fact action recognition, action prediction task requires action labels to be predicted from these partially observed videos. Our approach exploits abundant sequential context information to enrich the feature representations...
There has been incredible growth of events over the internet in recent years. Google has become the giant source of knowledge for any event which has happened or happening over the internet. Some networking sites such as face book, micro blogging sites such as twitter are evolved with time and became the highly used sites over the internet. Various E-commerce websites such as Amazon, Ebay, Flipkart...
In the quest of developing more accurate methodologies for Earth Observation (EO) image retrieval, visualization and information content exploration, a deep understanding of the data being analyzed is needed. In this paper we propose a simple but efficient visual data mining methodology that can be used for these tasks. Our solution consists in a patch-based feature extraction to derive image features...
Visual analytics plays a key role in bringing insights to audiences who are interested and dedicated in data exploration. In the area of relational data, many advanced visualization tools and frameworks are proposed in order to dealing with such data features. However, the majority of those have not greatly considered the whole process from data-model mining to query utilizing on dimensions and data...
Digital imaging plays an important role in many human activities, such as agriculture and forest management, earth sciences, urban planning, weather forecasting, medical imaging and so on. Processing, exploring and visualizing the inconceivable volumes of such images has turned out to be progressively troublesome. The Content-Based Image Retrieval (CBIR) remains an important issue that finds potential...
Retrieving information based on the users’ preferences and profiles represent a challenging issue to overcome. Moreover, in the public transport field, this task becomes increasingly complex due to the heterogeneous data fetched from various sources. Though, ontologies have emerged in retrieving information field to reduce this complexity. This paper describes a visual framework aiming...
In this paper, we propose an improved image retrieval method, dedicated to images of buildings/landmarks from urban environments. Locally detected key points are binary labelled as building or no-building using a SVM-based classifier. Thereafter, only key points labelled as building are retained. In this way, the data in the database vocabulary is reduced to only the relevant one and solely the relevant...
Video emotion recognition as an emerging research field has been attracting more and more focus in recent years. However, such work is quite challenging, since human emotions are hard to differentiate precisely due to its complexity and diversity, moreover, the expressions of sentiment in a content-rich video are sparse. Previous studies presented a number of approaches to try to learn human emotions...
In this paper, a novel line segment detection method based on probability map is proposed. Firstly, the local gradient information is used to estimate if a pixel belongs to a line segment and a probability map is produced. The probability map combines gradient orientation with gradient magnitude information and can provide candidate points for edge chain extraction. Secondly, these candidate points...
Accurate human body orientation estimation (HBOE) can significantly promote the analysis of human behavior. However, conventional methods cannot holistically exploit the complementary nature of spatial and temporal information for H-BOE. Different from existing methods, we propose an end-to-end temporal-spatial deep learning framework to accurately estimate the human body orientation. In this framework,...
Visual question answering (VQA) tasks use two types of images: abstract (illustrations) and real. Domain-specific differences exist between the two types of images with respect to “objectness,” “texture,” and “color.” Therefore, achieving similar performance by applying methods developed for real images to abstract images, and vice versa, is difficult. This is a critical problem in VQA, because image...
Higher Education Institutions store a sizable amount of data, including student records and the structure of a degree curriculum. This paper focuses on the problem of identifying how closely students follow the recommended order of the courses in a degree curriculum, and to what extent their performance is affected by the order they actually adopt. It addresses this problem by applying techniques...
Surgical workflow in minimally invasive interventions like laparoscopy can be modeled with the aid of tool usage information. The video stream available during surgery primarily for viewing the surgical site using an endoscope can be leveraged for this purpose without the need for additional sensors or instruments. We propose a method which learns to detect the tool presence in laparoscopy videos...
In this paper, we propose a novel deep end-to-end network to automatically learn the spatial-temporal fusion features for video-based person re-identification. Specifically, the proposed network consists of CNN and RNN to jointly learn both the spatial and the temporal features of input image sequences. The network is optimized by utilizing the siamese and softmax losses simultaneously to pull the...
Public sentiment is regarded as an important measure for event detection, information security, policy making etc. Analyzing public sentiments relies more and more on large amount of multimodal contents, in contrast to the traditional text-based and image-based sentiment analysis. However, most previous works directly extract feature from image as the additional information for text modality and then...
The results of visual inspection of welds in structures and machines depend on both the vision of the inspector and his/her measurement skills. The use of 3-D reconstruction technologies, as the structured light system (SLS), allows the accurate assessment of the weld bead according to the quality criteria. With the proposed procedure in this paper, the extraction of fundamental quality parameters...
Most of our current understanding of how programmers perform various software maintenance and evolution tasks is based on controlled studies or interviews, which are inherently limited in size, scope, and realism. Replicating controlled studies in the field can both explore the findings of these studies in wider contexts and study new factors that have not been previously encountered in the laboratory...
Existing approach to model sensor movement data as pairwise connections in networks implicitly assumes the Markov property and loses higher-order movement patterns. While the higher-order network (HON) captures higher-order movement patterns, there has not yet been a visualization tool tailored for HON. Based on our prior work, in this demo we present HoNVis, a comprehensive visualization and interactive...
With more proliferation of services and higher degree of personalization, higher accurate approaches to service recommendation are becoming more and more pivotal. Performance of existing service recommendation approaches is not satisfactory due to the sparseness of available data set or the incomplete information of the global service market, which make it difficult to identify a customer's potential...
This paper aims to provide a new method of visualizing high-dimensional data classification by employing principal component analysis (PCA) and support vector machine (SVM). In this method, PCA is adopted to reduce the dimension of high-dimensional data, and then SVM is used for the data classification process. At last, the classified result is projected to two-dimension mapping. The method can visualize...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.