On the application of detailed topological structure representation and deep feature fusion to multi-object image retrieval
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Abstract:
Spatial information representation is an important means to improve image visual feature representation performance. The integration of the spatial relation model with deep learning can effectively enhance semantic property of deep features, improving the image retrieval accuracy. In this paper, we proposed a novel detailed topological structure representation model to describe spatial relation of complex images. This model not only had complete topology description performance, but also provided two efficient reasoning algorithms, which made the topological invariants directly deduced from the model without any geometric calculations. Similarity matching approaches based on fine topological structure representation model was proposed for spatial relationship feature representation. Finally, in combination with convolution neural network, a multi-object image retrieval framework was developed by fusing the spatial relation features and deep features. Experimental results demonstrate that the proposed topological model has remarkable performance in spatial query. Moreover, the proposed image retrieval framework outperforms the current methods in terms of precision and with advantages of both the manual and deep features, it provides a superior means to improve interpretability of deep learning methods.
