Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons
International Journal of Computer Vision
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
International Journal of Computer Vision
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Representing shape with a spatial pyramid kernel
Proceedings of the 6th ACM international conference on Image and video retrieval
A comparison of color features for visual concept classification
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Foundations and Trends in Information Retrieval
Evaluation of GIST descriptors for web-scale image search
Proceedings of the ACM International Conference on Image and Video Retrieval
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In this paper we compare two state-of-the-art approaches for image classification. The first approach follows the Bag-of-Keypoints method for classifying images based on local image pattern frequency distribution. The second approach computes the gist of an image by computing global image statistics. Both approaches are explained in detail and their performance is compared using a subset of images taken from the ImageClef 2011 PhotoAnnotation task. The images were selected based on the assumption they could be better described using global features. Results show that while Bag-of-Keypoints-like classification performs better even for global concepts the classification accuracy of the global descriptor remains acceptable at a much smaller computational footprint.