Abstract
Aiming at multi-class large-scale image retrieval problem, a new image retrieval method based on classification probability estimation is proposed according to the thinking named “Classification First, Retrieval Later”. According to the method, the image features are effectively fused using a composite kernel method first, and a composite kernel classifier with higher classification precision is designed. The optimal coefficients of the classifier are also obtained utilizing the classification result with small-amount image samples. Second, complete the classification probability estimation for the testing images using the composite machine. Third, realize the image retrieval based on the classification probability estimation values. In the experiments with multi-class large-scale image dataset, it is confirmed that the presented method can achieve better retrieval precision. Moreover, the generalization performance without prior knowledge is also studied.
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Acknowledgments
This work was jointly supported by the National Natural Science Foundation for Young Scientists of China (Grant No: 61202332) and China Postdoctoral Science Foundation (Grant No: 2012M521905).
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Wang, H., Cai, Y., Wang, S., Fu, G., Li, L. (2014). Classification Probability Estimation Based Multi-Class Image Retrieval. In: Sun, F., Li, T., Li, H. (eds) Knowledge Engineering and Management. Advances in Intelligent Systems and Computing, vol 214. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37832-4_36
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DOI: https://doi.org/10.1007/978-3-642-37832-4_36
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