[[abstract]]Object recognition based on semantic contents of images is more reasonable than that based on low-level image features. In order to bridge the semantic gap between low-level image features and high-level concepts in human cognition, we presents an unsupervised approach to build a new image representation, which is called probabilistic semantic component descriptor (pSCD), by collecting high-level concepts from images. We first quantize low-level features into a set of visual words, and then we apply a revised model of probabilistic Latent Semantic Analysis (pLSA) to analyze what kinds of hidden concepts between visual words and images are involved. After collecting these discovered concepts, we could build pSCD for object recognition. We also discuss how many hidden concepts are appropriate for pSCD to describe a set of images. Finally, through object recognition experiments, we demonstrate that pSCD is more discriminative than other image representations, including Bag-of-Words (BoW) and pLSA representations.
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