Designing for video: investigating the contextual cues within viewing situations

The viewing of video increasingly occurs in a wide range of public and private environments via a range of static and mobile devices. The proliferation of content on demand and the diversity of the viewing situations means that delivery systems can play a key role in introducing audiences to contextually relevant content of interest whilst maximising the viewing experience for individual viewers. However, for video delivery systems to do this, they need to take into account the diversity of the situations where video is consumed, and the differing viewing experiences that users desire to create within them. This requires an ability to identify different contextual viewing situations as perceived by users. This paper presents the results from a detailed, multi-method, user-centred field study with 11 UK-based users of video-based content. Following a review of the literature (to identify viewing situations of interest on which to focus), data collection was conducted comprising observation, diaries, interviews and self-captured video. Insights were gained into whether and how users choose to engage with content in different public and private spaces. The results identified and validated a set of contextual cues that characterise distinctive viewing situations. Four archetypical viewing situations were identified: ‘quality time’, ‘opportunistic planning’, ‘sharing space but not content’ and ‘opportunistic self-indulgence’. These can be differentiated in terms of key contextual factors: solitary/shared experiences, public/private spaces and temporal characteristics. The presence of clear contextual cues provides the opportunity for video delivery systems to better tailor content and format to the viewing situation or additionally augment video services through social media in order to provide specific experiences sensitive to both temporal and physical contexts.     

Adaptive-based methods for information transmission by means of chaotic signal source modulation

Three methods for information transmission based on chaotic signal source modulation based on: application of adaptive observers, adaptive identification with the Implicit Adjustable Model, and frequency modulation with the adaptive demodulator are described. Application of the proposed methods for information transmission by means of chaotic Chua generator modulation is presented.     

Ranking kinematics for revising by contextual information

Annals of Mathematics and Artificial Intelligence - Probability kinematics is a leading paradigm in probabilistic belief change. It is based on the idea that conditional beliefs should be...     

Privileged contextual information for context-aware recommender systems

A recommender system is used in various fields to recommend items of interest to the users. Most recommender approaches focus only on the users and items to make the recommendations. However, in many applications, it is also important to incorporate contextual information into the recommendation process. Although the use of contextual information has received great focus in recent years, there is a lack of automatic methods to obtain such information for context-aware recommender systems. Some works address this problem by proposing supervised methods, which require greater human effort and whose results are not so satisfactory. In this scenario, we propose an unsupervised method to extract contextual information from web page content. Our method builds topic hierarchies from page textual content considering, besides the traditional bag-of-words, valuable information of texts as named entities and domain terms (privileged information). The topics extracted from the hierarchies are used as contextual information in context-aware recommender systems. We conducted experiments by using two data sets and two baselines: the first baseline is a recommendation system that does not use contextual information and the second baseline is a method proposed in literature to extract contextual information. The results are, in general, very good and present significant gains. In conclusion, our method has advantages and innovations:(i) it is unsupervised; (ii) it considers the context of the item (Web page), instead of the context of the user as in most of the few existing methods, which is an innovation; (iii) it uses privileged information in addition to the existing technical information from pages; and (iv) it presented good and promising empirical results. This work represents an advance in the state-of-the-art in context extraction, which means an important contribution to context-aware recommender systems, a kind of specialized and intelligent system.     

Use of contextual information for feature ranking anddiscretization

Deriving classification rules or decision trees from examples is an important problem. When there are too many features, discarding weak features before the derivation process is highly desirable. When there are numeric features, they need to be discretized for the rule generation. We present a new approach to these problems. Traditional techniques make use of feature merits based on either the information theoretic, or the statistical correlation between each feature and the class. We instead assign merits to features by finding each feature's “obligation” to the class discrimination in the context of other features. The merits are then used to rank the features, select a feature subset, and discretize the numeric variables. Experience with benchmark example sets demonstrates that the new approach is a powerful alternative to the traditional methods. This paper concludes by posing some new technical issues that arise from this approach     

Annotation processes for flexible management of contextual information

We propose the use of annotations as a way to flexibly enrich a domain of interest with information concerning different contexts of use for its elements. We provide a formal model of annotation in the framework of typed graphs, in which the presence of annotations is reified through nodes and edges of specific types, relating nodes from different domains. This allows the flexible activation and de-activation of annotations, as well as the addition of several annotations from different domains on the same element. We show that annotations give rise to a category, where pushouts are the basic construct for the composition of annotation-related processes. We prove some properties of annotated graphs and discuss examples drawn from several fields.     

Disambiguating visual motion through contextual feedback modulation

Motion of an extended boundary can be measured locally by neurons only orthogonal to its orientation (aperture problem) while this ambiguity is resolved for localized image features, such as corners or nonocclusion junctions. The integration of local motion signals sampled along the outline of a moving form reveals the object velocity. We propose a new model of V1-MT feedforward and feedback processing in which localized V1 motion signals are integrated along the feedforward path by model MT cells. Top-down feedback from MT cells in turn emphasizes model V1 motion activities of matching velocity by excitatory modulation and thus realizes an attentional gating mechanism. The model dynamics implement a guided filling-in process to disambiguate motion signals through biased on-center, off-surround competition. Our model makes predictions concerning the time course of cells in area MT and V1 and the disambiguation process of activity patterns in these areas and serves as a means to link physiological mechanisms with perceptual behavior. We further demonstrate that our model also successfully processes natural image sequences.     

Conditional random field with the multi-granular contextual information for pixel labeling

To make full use of the contextual information object recognition and scene understanding, a multi-granular context conditional random field (MGCCRF) model is presented to combine context information in a variety of scales. It is efficiently implemented through extending the pairwise clique to the multi-granular context windows. In the fine-granular context window, the label consistency of similar features can be obtained with the probability of the label transferring between two adjacent pixels. At the same time, the spatial relationships among different classes in the coarse-granular context window are explicated in details. To train the MGCCRF model, a piecewise training method with the bound optimization algorithm is designed to improve the performance. Experiments on two real-world image databases show that compared with other methods, the modified conditional random field model is more competitive and effective in terms of the quantitative and qualitative labeling performance.     

Exploring contextual information for view-wised 3D model retrieval

Multimedia Tools and Applications - Recently, with the rapid development of digital technologies and its wide application, 3D model retrieval is becoming more and more important in graphic...     

融合上下文信息的场景结构恢复

提出了一种融合场景上下文信息的两级分类算法,从单幅图像中恢复场景结构。室外场景的结构化特征使其3维结构可以粗略地分为3类:"地面","天空"以及"竖直物体"。首先,把图像分割成具有灰度和颜色一致性的区域;其次确定特征显著区域("确定区域")的结构,将特征不明显的区域标记为"未知区域";然后根据"未知区域"与"确定区域"的相似性及"确定区域"场景结构对"未知区域"的可能结构进行投票,将投票最多的结构类型赋予"未知区域";最后介绍场景结构恢复在构造场景3维模型方面的应用。实验结果表明,由于利用了场景结构的上下文信息,该算法场景结构恢复的正确率为92.3%,优于现有算法88.1%的恢复正确率。     

PostBits: using contextual locations for embedding cloud information in the home

Placing information at specific locations in the home provides rich and intuitive ways for people to cope with information, as they leverage semantics of the locations within the home. However, there is no deeper investigation yet on how users would embed digital cloud-based information into various locations in their homes, partly because previous systems were not robust enough to be deployed in real settings for an extended period of time. To this end, we have developed PostBits, a system of display blocks that integrate cloud information with contextually rich physical space. PostBits were designed for long battery life, robust communication and simple interactions, to enable a field deployment. A field study was conducted with 6 families, each using the system in their home for 1 week. We have identified patterns and strategies of how users embed cloud information at contextual locations in the home, and reflect on future design opportunities.     

Enriching machine-mediated speech-to-speech translation using contextual information

Conventional approaches to speech-to-speech (S2S) translation typically ignore key contextual information such as prosody, emphasis, discourse state in the translation process. Capturing and exploiting such contextual information is especially important in machine-mediated S2S translation as it can serve as a complementary knowledge source that can potentially aid the end users in improved understanding and disambiguation. In this work, we present a general framework for integrating rich contextual information in S2S translation. We present novel methodologies for integrating source side context in the form of dialog act (DA) tags, and target side context using prosodic word prominence. We demonstrate the integration of the DA tags in two different statistical translation frameworks, phrase-based translation and a bag-of-words lexical choice model. In addition to producing interpretable DA annotated target language translations, we also obtain significant improvements in terms of automatic evaluation metrics such as lexical selection accuracy and BLEU score. Our experiments also indicate that finer representation of dialog information such as yes–no questions, wh-questions and open questions are the most useful in improving translation quality. For target side enrichment, we employ factored translation models to integrate the assignment and transfer of prosodic word prominence (pitch accents) during translation. The factored translation models provide significant improvement in assignment of correct pitch accents to the target words in comparison with a post-processing approach. Our framework is suitable for integrating any word or utterance level contextual information that can be reliably detected (recognized) from speech and/or text.     

An augmented reality interface to contextual information

In this paper, we report on a prototype augmented reality (AR) platform for accessing abstract information in real-world pervasive computing environments. Using this platform, objects, people, and the environment serve as contextual channels to more information. The user??s interest with respect to the environment is inferred from eye movement patterns, speech, and other implicit feedback signals, and these data are used for information filtering. The results of proactive context-sensitive information retrieval are augmented onto the view of a handheld or head-mounted display or uttered as synthetic speech. The augmented information becomes part of the user??s context, and if the user shows interest in the AR content, the system detects this and provides progressively more information. In this paper, we describe the first use of the platform to develop a pilot application, Virtual Laboratory Guide, and early evaluation results of this application.     

Robust image recognition by fusion of contextual information

This paper studies the fusion of contextual information in pattern recognition, with applications to biomedical image identification. In the real world there are cases where the identity of an object is ambiguous if the classification is based only on its own features. It is helpful to reduce the ambiguity by utilizing extra information, referred to as context, provided by accompanying objects. We investigate two techniques that incorporate context. The first approach, based on compound Bayesian theory, incorporates context by fusing the measurements of all objects under consideration. It is an optimal strategy in terms of achieving minimum set-by-set error probability. The second approach fuses the measurements of an object with explicitly extracted context. Its linear computational complexity makes it more tractable than the first approach, which requires exponential computation. These two techniques are applied to two medical applications: white blood cell image classification and microscopic urinalysis. It is demonstrated that superior classification performances are achieved by using context. In our particular applications, it reduces overall classification error, as well as false positive and false negative diagnosis rates.     

Relative image similarity learning with contextual information for Internet cross-media retrieval

With the fast explosive rate of the amount of image data on the Internet, how to efficiently utilize them in the cross-media scenario becomes an urgent problem. Images are usually accompanied with contextual textual information. These two heterogeneous modalities are mutually reinforcing to make the Internet content more informative. In most cases, visual information can be regarded as an enhanced content of the textual document. To make image-to-image similarity being more consistent with document-to-document similarity, this paper proposes a method to learn image similarities according to the relations of the accompanied textual documents. More specifically, instead of using the static quantitative relations, rank-based learning procedure by employing structural SVM is adopted in this paper, and the ranking structure is established by comparing the relative relations of textual information. The learning results are in more accordance with the human’s recognition. The proposed method in this paper can be used not only for the image-to-image retrieval, but also for cross-modality multimedia, where a query expansion framework is proposed to get more satisfactory results. Extensive experimental evaluations on large scale Internet dataset validate the performance of the proposed methods.     

CI-SNF: Exploiting contextual information to improve SNF based information retrieval

Similarity networks contain important topological features and patterns critical to understanding interactions among samples in a large dataset. To create a comprehensive view of the interactions within a dataset, the Similarity Network Fusion (SNF) technique has been proposed to fuse the similarity networks based on different data types into one similarity network that represents the full spectrum of underlying data. In this paper, a modified version of SNF, which is named as Contextual Information based SNF (CI-SNF), is proposed. In CI-SNF, first, modified Jaccard distance is performed on the SNF fused similarity to utilize the contextual information contained in the fused similarity network. Second, the local consistency of samples from the same category is enhanced by speculating that the samples which are located high in the Jaccard distance based ranking list of a specific query are from the same category as the query. Third, the inverted index technique is introduced to utilize the sparsity property of the locally consistent similarity network to enhance the computational efficiency. To verify the effectiveness and efficiency of CI-SNF model, it is applied in four different tasks, Cover Song Identification (CSI), image classification, cancer subtype identification, and drug taxonomy, respectively. Extensive experiments on thirteen challenging datasets demonstrate that CI-SNF scheme outperforms state-of-the-art similarity fusion algorithms including SNF in all four tasks. It is also verified that utilizing the contextual information contained in the SNF-based similarity network helps to enhance the performance of the SNF-based scheme, further.     

面向输电线故障信息传输的多播路由

针对无线传感器网络应用于输电线路故障传输时存在通信代价高、实时性差的问题,提出一种输电线路故障传输多播路由算法(MRFT)。抽象出输电线路故障信息传输网络模型;根据时延最短路径树(SPT)的最大端到端时延确定多播树时延上限,将时延上限边接入多播树;设计最小代价启发函数将剩余叶子节点接入多播树。仿真结果表明,与KPP算法相比,MRFT算法构造的多播树在多播树时延、端到端时延方差和多播树代价3个方面均有良好表现。该算法能够有效保证输电线路故障信息传输的实时性,降低通信代价。     

Time-optimized contextual information forwarding in mobile sensor networks

We study on the forwarding of quality contextual information in mobile sensor networks (MSNs). Mobile nodes form ad-hoc distributed processing networks that produce accessible and quality-stamped information about the surrounding environment. Due to the dynamic network topology of such networks the context quality indicators seen by the nodes vary over time. A node delays the context forwarding decision until context of better quality is attained. Moreover, nodes have limited resources, thus, they have to balance between energy conservation and quality of context. We propose a time-optimized, distributed decision making model for forwarding context in a MSN based on the theory of optimal stopping. We compare our findings with certain context forwarding schemes found in the literature and pinpoint the advantages of the proposed model.