Rapid Biologically-Inspired Scene Classification Using Features Shared with Visual Attention
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scene Categorization by Introducing Contextual Information to the Visual Words
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Scene categorization via contextual visual words
Pattern Recognition
Semi-supervised metric learning for image classification
PCM'10 Proceedings of the Advances in multimedia information processing, and 11th Pacific Rim conference on Multimedia: Part II
Hi-index | 0.00 |
We present context-based scene recognition for mobile robotics applications. Our classifier is able to differentiate outdoor scenes without temporal filtering relatively well from a variety of locations at a college campus using a set of features that together capture the "gist" of the scene. We compare the classification accuracy of a set of scenes from 1551 frames filmed outdoors along a path and dividing them to four and twelve different legs while obtaining a classifi- cation rate of 67.96 percent and 48.61 percent, respectively. We also tested the scalability of the features by comparing the classification results from the previous scenes with four legs with a longer path with eleven legs while obtaining a classification rate of 55.08 percent. In the end, some ideas are put forth to improve the theoretical strength of the gist features.