Abstract
Due to the semantic gap between visual features and semantic concepts, automatic image annotation has become a difficult issue in computer vision recently. We propose a new image multilabel annotation method based on double-layer probabilistic latent semantic analysis (PLSA) in this paper. The new double-layer PLSA model is constructed to bridge the low-level visual features and high-level semantic concepts of images for effective image understanding. The low-level features of images are represented as visual words by Bag-of-Words model; latent semantic topics are obtained by the first layer PLSA from two aspects of visual and texture, respectively. Furthermore, we adopt the second layer PLSA to fuse the visual and texture latent semantic topics and achieve a top-layer latent semantic topic. By the double-layer PLSA, the relationships between visual features and semantic concepts of images are established, and we can predict the labels of new images by their low-level features. Experimental results demonstrate that our automatic image annotation model based on double-layer PLSA can achieve promising performance for labeling and outperform previous methods on standard Corel dataset.
Highlights
With the advent of the information era, the usage of the Internet is increasingly prevalent and the scale of the multimedia database is fast growing
The existence of the semantic gap [1] leads to the fact that the images with similar visual characteristics may not be similar in semantics
We propose a new multilabel annotation method of images based on double-layer Probabilistic Latent Semantic Analysis (PLSA) model, which applies relevant knowledge of latent topic space for image semantic annotation
Summary
With the advent of the information era, the usage of the Internet is increasingly prevalent and the scale of the multimedia database is fast growing. The existence of the semantic gap [1] leads to the fact that the images with similar visual characteristics may not be similar in semantics. For solving this problem, many image automatic annotation methods have been proposed for largescale image understanding. Inspired by the techniques of text analysis, probability topic models are used to learn the relationships between the low-level visual features and high-level semantic concepts for automatic image annotation. Using probability topic model can effectively map highdimensional image feature vectors to a low-dimensional space, greatly reducing the redundant information of the image and the time complexity of the algorithm. The LDA topic model exploits complex bayesian structure and needs to determine prior parameters of the model, which makes its applicability less wide than PLSA model
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