Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.

An efficient approach for scene classification is necessary for automatically labeling an image as well as for retrieval of desired images from large scale repositories. In this paper machine learning and computer vision techniques have been applied for scene classification. The system is based on feature fusion method with holistic visual color, texture and edge descriptors. Color moments, Color Coherence Vector, Color Auto Correlogram, GLCM, Daubechies Wavelets, Gabor filters and MPEG-7 Edge Direction Histogram have been used in the proposed system to find the best combination of features for this problem. Two state-ofthe-art soft computing machine learning techniques: Support vector machine (SVM) and Artificial Neural Networks have been used to classify scene images into meaningful categories. The benchmarked Oliva-Torralba dataset has been used in this research. We report satisfactory categorization performances on a large data set of eight categories of 2688 complex, natural and urban scenes. Using a set of exhaustive experiments our proposed system has achieved classification accuracy as high as 92.5% for natural scenes (OT4) and as high as 86.4% for mixed scene categories (OT8). We also evaluate the system performance by predictive accuracy measures namely sensitivity, specificity, F-score and kappa statistic.

A challenging problem of computer vision is scene classification. An efficient method for classifying natural scenes from the Oliva -Torralba dataset is proposed. The method is based on K-Means clustering algorithm followed by a novel two phase voting method for classification which is the main contribution of this paper. Two distinct feature sets have been used. The first feature set is used for grouping perceptually similar images into two clusters based on KMeans algorithm. The second feature set is selected based on observed visual attributes of images in these two clusters. Classification is achieved by a novel voting method which firstly assigns test image to the most similar cluster. Each cluster contains images from four categories. Therefore to assign test image to correct category within an assigned cluster, candidate voters from the assigned cluster are selected. The category of majority candidate voters decides the class of test image. The efficiency of the proposed voting scheme is that 83.4% test images are correctly classified. Silhouette index, purity, variance, F-measure and Rand's metric are used for cluster validation.

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Text mining is an emerging technology that can be used to augment existing data in corporate databases by making unstructured text data available for analysis. The incredible increase in online documents, which has been mostly due to the expanding internet, has renewed the interest in automated document classification and data mining. The demand for text classification to aid the analysis and management of text is increasing. Text is cheap, but information, in the form of knowing what classes a text belongs to, is expensive. Text classification is the process of classifying documents into predefined categories based on their content. Automatic classification of text can provide this information at low cost, but the classifiers themselves must be built with expensive human effort, or trained from texts which have themselves been manually classified. Both classification and association rule mining are indispensable to practical applications. For association rule mining, the target of discovery is not pre-determined, while for classification rule mining there is one and only one predetermined target. Thus, great savings and conveniences to the user could result if the two mining techniques can somehow be integrated. In this paper, such an integrated framework, called associative classification is used for text categorization The algorithm presented here for text classification uses words as features , to derive feature set from preclassified text documents. The concept of Naïve Bayes classifier is then used on derived features for final classification.