Modeling Hidden Topics with Dual Local Consistency for Image Analysis

Image representation is the crucial component in image analysis and understanding. However, the widely used low-level features cannot correctly represent the high-level semantic content of images in many situations due to the “semantic gap”. In order to bridge the “semantic gap”, in this brief, we present a novel topic model, which can learn an effective and robust mid-level representation in the latent semantic space for image analysis. In our model, the ℓ

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-graph is constructed to model the local image neighborhood structure and the word co-occurrence is computed to capture the local word consistency. Then, the local information is incorporated into the model for topic discovering. Finally, the generalized EM algorithm is used to estimate the parameters. As our model considers both the local image structure and local word consistency simultaneously when estimating the probabilistic topic distributions, the image representations can have more powerful description ability in the learned latent semantic space. Extensive experiments on the publicly available databases demonstrate the effectiveness of our approach.