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The purpose of this paper is to build a classification system which mimics the perceptual ability of human vision, in gathering knowledge about the structure, content and the surrounding environment of a real-world natural scene, at a quick glance accurately. This paper proposes a set of novel features to determine the gist of a given scene based on dominant color, dominant direction, openness and roughness features.

The classification system is designed at two different levels. At the first level, a set of low level features are extracted for each semantic feature. At the second level the extracted features are subjected to the process of feature evaluation, based on inter-class and intra-class distances. The most discriminating features are retained and used for training the support vector machine (SVM) classifier for two different data sets.

Accuracy of the proposed system has been evaluated on two data sets: the well-known Oliva-Torralba data set and the customized image data set comprising of high-resolution images of natural landscapes. The experimentation on these two data sets with the proposed novel feature set and SVM classifier has provided 92.68 percent average classification accuracy, using ten-fold cross validation approach. The set of proposed features efficiently represent visual information and are therefore capable of narrowing the semantic gap between low-level image representation and high-level human perception.

The method presented in this paper represents a new approach for extracting low-level features of reduced dimensionality that is able to model human perception for the task of scene classification. The methods of mapping primitive features to high-level features are intuitive to the user and are capable of reducing the semantic gap. The proposed feature evaluation technique is general and can be applied across any domain.

Improving mental health literacy is a key component of any population-based mental health program, especially in low- and middle-income countries. Effective strategies to increase awareness and reduce stigma associated with mental health are sparse and have not been evaluated in India or among other low- and middle-income countries. The paper aims to discuss these issues.

The review was based on the literature obtained from articles identified by searches of Medline, PubMed, and Google (Scholar) with the Mesh terms “mental health literacy”, “developing countries,” and “audience segmentation” between 1979 and 2012. Information was also obtained by interacting with experts in the field of health communication and public health, one of whom (M.K.) is a co-author.

Systematic reviews of studies among occidental countries have proposed that targeted approaches to mental health literacy are not only more effective, but also more cost-effective than general population approaches. Using audience segmentation to target distinct population sub-groups is a well-established best practice in health communication, is recommended for low resource settings and in situations with a limited budget, and may be especially effective when based on socio-cultural variables.

Yet to date it has not been applied in India for mental-health-related communication. The need for such cost-effective, innovative, and equitable strategies for mental health literacy is the cornerstone to mitigate stigma associated with mental illness, and improve awareness among a proportionately illiterate population.