Research On Scene Classification Technologies With The Local Context Feature And Spatial Pyramid Model

The process of scene image classification is that how to make computer systems toclassify the image sets automatically which contain semantic information, according to thevisual perception mechanism of human. Scene classification has become an activeresearch topic in the computer vision area, which provides important environmental cluesfor object recognition and other computer vision tasks. Be similar to the words in text data,modeling scene images with visual vocabulary could form a middle representation, whichdescribes the semantic information of scene images effectively. Based on the bag-of-wordsmodel of scene images, we focus on the feature extraction, visual words formation andvisual words representation to do the following research:First of all, the traditional visual words are formed by local features independently,and consider nothing about the relations among of features. To overcome this defect, wepropose a kind of local visual features that include multi-direction context information,and use the category-specific strategy to form the visual vocabulary, after that the spatialpyramid model is combined to accomplish the scene classification. According to differentscene categories, this method combines the feature similarity and contextual relationtogether. The experiments show that this method performs better than the existed methods.Secondly, since the context relations play an important role in the featurerepresentation of images, we do some further research about the effectiveness of contextinformation. Utilizing the flatness information of image regions, a new feature extractionmethod is proposed to form adaptive context features in an unsupervised manner, and thevisual words are formed by the specific categories of images. After that the integrateddistribution vectors of visual words are computed by applying the spatial pyramid modelwhich based on sparse coding, and the scene classification is accomplished by thosedistribution vectors. This method chooses the effective context features and theexperiment results show that it could achieve a higher accuracy obviously.Finally, in order to extract the further visual property of scene images in differentaspects, we introduce a local self-similarity descriptor to describe scene images on the basis of adaptive context features. Then an image representation method is proposed bycombining the two complementary features, and the visual words model is obtained byusing the category-specific strategy and sparse coding, after that a discriminative spatialpyramid representation is exploited by applying partial least squares theory to accomplishthe scene classification. This method could make the bag-of words model more flexibleand discriminative. The experiment results show that this method could achieve a higheraccuracy, especially perform well in complicated indoor images.