Efficient approach for scene understanding by low confidence region boosting

The focus of this paper is providing a semantic description of images, where each pixel is assigned a label from a pre-defined set of categories. The human visual system is very efficient in recognizing difficult content, making guesses and predictions reinforced by the contextual relationships of neighboring visual stimuli. Motivated by this notion, we approach the image labeling problem by searching for regions with high classification confidence, and use spatial dependencies to improve decisions about the uncertain ones. The higher the number of high confidence predictions, the more relevant information is available and thus a better overall classification can be achieved. Image regions are tested against separate SVM classifiers, and the final labels are computed by applying a post-processing algorithm to the probability outputs. The proposed method is computationally simpler compared to the trending use of graphical models, and experimental results show that it is especially effective in cases of small training sets, achieving results comparable to the state-of-the-art algorithms.