-
Upload Video
videos in mp4/mov/flv
close
Upload video
Note: publisher must agree to add uploaded document -
Upload Slides
slides or other attachment
close
Upload Slides
Note: publisher must agree to add uploaded document -
Feedback
help us improve
close
Feedback
Please help us improve your experience by sending us a comment, question or concern
Please help transcribe this video using our simple transcription tool. You need to be logged in to do so.
Description
Histogram-based features have significantly contributed to recent development of image classifications, such as by SIFT local descriptors. In this paper, we propose a method to efficiently transform those histogram features for improving the classification performance. The (L1 -normalized) histogram feature is regarded as a probability mass function, which is modeled by Dirichlet distribution. Based on the probabilistic modeling, we induce the Dirichlet Fisher kernel for transforming the histogram feature vector. The method works on the individual histogram feature to enhance the discriminative power at a low computational cost. On the other hand, in the bag-of-feature (BoF) frame- work, the Dirichlet mixture model can be extended to Gaussian mixture by transforming histogram-based local descriptors, e.g., SIFT, and thereby we propose the method of Dirichlet-derived GMM Fisher kernel. In the experiments on diverse image classification tasks including recognition of subordinate objects and material textures, the pro- posed methods improve the performance of the histogram- based features and BoF-based Fisher kernel, being favor- ably competitive with the state-of-the-arts.