q-Gaussian mixture models for image and video semantic indexing
We propose a q-Gaussian mixture model (q-GMM) for image and video semantic indexing. The q-GMM has a parameter q that controls its tail-heaviness. The q-GMM is more suitable than a GMM for representing images and videos. Our proposed method outperformed bag-of-visual-words on PASCAL VOC and TRECVID datasets.
q-gaussian mixture models semantic indexing gaussian mixture models gmm supervector image classification bag-of-words codebook tsallis statistics