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Context-aware and locality-constrained coding for image categorization.

ABSTRACT: Improving the coding strategy for BOF (Bag-of-Features) based feature design has drawn increasing attention in recent image categorization works. However, the ambiguity in coding procedure still impedes its further development. In this paper, we introduce a context-aware and locality-constrained Coding (CALC) approach with context information for describing objects in a discriminative way. It is generally achieved by learning a word-to-word cooccurrence prior to imposing context information over locality-constrained coding. Firstly, the local context of each category is evaluated by learning a word-to-word cooccurrence matrix representing the spatial distribution of local features in neighbor region. Then, the learned cooccurrence matrix is used for measuring the context distance between local features and code words. Finally, a coding strategy simultaneously considers locality in feature space and context space, while introducing the weight of feature is proposed. This novel coding strategy not only semantically preserves the information in coding, but also has the ability to alleviate the noise distortion of each class. Extensive experiments on several available datasets (Scene-15, Caltech101, and Caltech256) are conducted to validate the superiority of our algorithm by comparing it with baselines and recent published methods. Experimental results show that our method significantly improves the performance of baselines and achieves comparable and even better performance with the state of the arts.

SUBMITTER: Xiao W

PROVIDER: S-EPMC3977552 | biostudies-other | 2014

REPOSITORIES: biostudies-other

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Project description:BackgroundContext-aware systems, also known as context-sensitive systems, are computing applications designed to capture, interpret, and use contextual information and provide adaptive services according to the current context of use. Context-aware systems have the potential to support patients with chronic conditions; however, little is known about how such systems have been utilized to facilitate patient work.ObjectiveThis study aimed to characterize the different tasks and contexts in which context-aware systems for patient work were used as well as to assess any existing evidence about the impact of such systems on health-related process or outcome measures.MethodsA total of 6 databases (MEDLINE, EMBASE, CINAHL, ACM Digital, Web of Science, and Scopus) were scanned using a predefined search strategy. Studies were included in the review if they focused on patients with chronic conditions, involved the use of a context-aware system to support patients' health-related activities, and reported the evaluation of the systems by the users. Studies were screened by independent reviewers, and a narrative synthesis of included studies was conducted.ResultsThe database search retrieved 1478 citations; 6 papers were included, all published from 2009 onwards. The majority of the papers were quasi-experimental and involved pilot and usability testing with a small number of users; there were no randomized controlled trials (RCTs) to evaluate the efficacy of a context-aware system. In the included studies, context was captured using sensors or self-reports, sometimes involving both. Most studies used a combination of sensor technology and mobile apps to deliver personalized feedback. A total of 3 studies examined the impact of interventions on health-related measures, showing positive results.ConclusionsThe use of context-aware systems to support patient work is an emerging area of research. RCTs are needed to evaluate the effectiveness of context-aware systems in improving patient work, self-management practices, and health outcomes in chronic disease patients.

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Context-aware and locality-constrained coding for image categorization.

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