Analysis of interest point distribution in SURF octaves

Speeded-Up Robust Features (SURF) is a state-ofthe- art, scale- and rotation-invariant feature extraction technique with the potential for real-time execution. Although SURF has been extensively employed for multi-scale computer vision applications since its inception, there are still some areas of this computationally complex algorithm that have not been fully explored and require detailed analysis to enable algorithm-level optimization of SURF for real-time execution. In particular, the distribution of interest points in SURF octaves is a topic that requires thorough investigation. Contrary to the present perception, this paper demonstrates that there is a possibility of higher octaves being more significant than the lower octaves in terms of detected interest points for real-life images. The paper also shows that variation of blob response threshold has a significant effect on interest point distribution. The results presented highlight the need of developing a systematic approach to SURF octave selection

Perceptual-based textures for scene labeling: a bottom-up and a top-down approach

Due to the semantic gap, the automatic interpretation of digital images is a very challenging task. Both the segmentation and classification are intricate because of the high variation of the data. Therefore, the application of appropriate features is of utter importance. This paper presents biologically inspired texture features for material classification and interpreting outdoor scenery images. Experiments show that the presented texture features obtain the best classification results for material recognition compared to other well-known texture features, with an average classification rate of 93.0%. For scene analysis, both a bottom-up and top-down strategy are employed to bridge the semantic gap. At first, images are segmented into regions based on the perceptual texture and next, a semantic label is calculated for these regions. Since this emerging interpretation is still error prone, domain knowledge is ingested to achieve a more accurate description of the depicted scene. By applying both strategies, 91.9% of the pixels from outdoor scenery images obtained a correct label