Abstract
Approximate near neighbor search plays a critical role in various kinds of multimedia applications. The vocabulary-based hashing scheme uses vocabularies, i.e. selected sets of feature points, to define a hash function family. The function family can be employed to build an approximate near neighbor search index. The critical problem in vocabulary-based hashing is the criteria of choosing vocabularies. This paper proposes a approach to greedily choosing vocabularies via Adaboost. An index quality criterion is designed for the AdaBoost approach to adjust the weight of the training data. We also describe the parallelized version of the index for large scale applications. The promising results of the near-duplicate image detection experiments show the efficiency of the new vocabulary construction algorithm and desired qualities of the parallelized vocabulary-based hashing for large scale applications.
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References
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Liang, Y., Li, J., Zhang, B. (2010). Learning Vocabulary-Based Hashing with AdaBoost. In: Boll, S., Tian, Q., Zhang, L., Zhang, Z., Chen, YP.P. (eds) Advances in Multimedia Modeling. MMM 2010. Lecture Notes in Computer Science, vol 5916. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11301-7_54
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DOI: https://doi.org/10.1007/978-3-642-11301-7_54
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