Bidirectional-isomorphic manifold learning at image semantic understanding & representation

From relevant textual information to improve visual content understanding and representation is an effective way for deeply understanding web image content. However, the description of images is usually imprecise at the semantic level, which is caused by the noisy and redundancy information in both text (such as surrounding text in HTML pages) and visual (such as intra-class diversity) aspects. This paper considers the solution from the association analysis for image content and presents a Bidirectional- Isomorphic Manifold learning strategy to optimize both visual feature space and textual space, in order to achieve more accurate comprehension for image semantics and relationships. To achieve this optimization between two different models, Bidirectional-Isomorphic Manifold Learning utilizes a novel algorithm to unify adjustments in both models together to a topological structure, which is called the reversed Manifold mapping. We also demonstrate its correctness and convergence from a mathematical perspective. Image annotation and keywords correlation analysis are applied. Two groups of experiments are conducted: The first group is carried on the Corel 5000 image database to validate our method's effectiveness by comparing with state-of-the-art Generalized Manifold Ranking Based Image Retrieval and SVM, while the second group carried on a web-downloaded Flickr dataset with over 6,000 images to testify the proposed method's effectiveness in real-world application. The promising results show that our model attains a significant improvement over state-of-the-art algorithms.