Abstract
This paper proposes a fast, incremental codebook quantization algorithm for image classification consisting of a fast codebook graph learning algorithm using incremental neural learning, and a subgraph-based coding method. Comparing with the algorithms based on classic Bag-of-Features (BOF) model, it holds the following advantages: 1) it learns codebook fast and effectively simply using a few training data; 2) it models relationships among visual words to guarantee higher discriminative power; 3) it automatically learns codebook with appropriate size. The above characteristics make our method more suitable for handling large-scale image classification tasks. Experimental results on Caltech-101 and Caltech-256 datasets demonstrate that the proposed algorithm achieves better performance while decreasing the computational cost remarkably.
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Tang, Y., Yang, YB., Gao, Y., Zhang, Y., Cao, YC. (2012). Codebook Quantization for Image Classification Using Incremental Neural Learning and Subgraph Extraction. In: Yin, H., Costa, J.A.F., Barreto, G. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2012. IDEAL 2012. Lecture Notes in Computer Science, vol 7435. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32639-4_34
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DOI: https://doi.org/10.1007/978-3-642-32639-4_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32638-7
Online ISBN: 978-3-642-32639-4
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