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• W2565504097 abstract "Recently, hashing based technique has attracted much attention in media search community. In many applications, data have multiple modalities and multiple labels. Many hashing methods have been proposed for multi-modal data; however, they seldom consider the scenario of multiple labels or only use such information to build a simple similarity matrix, e.g., the corresponding value is 1 when two samples share at least one same label. Apparently, such methods cannot make full use of the information contained in multiple labels. Thus, a model is expected to have good performance if it can make full use of information in multi-modal and multi-label data. Motivated by this, in this paper, we propose a new method, multi-modal multi-label hashing-M3LH, which can not only work on multi-modal data, but also make full use of information contained in multiple labels. Specifically, in M3LH, we assume every label is associated with a binary code in Hamming space, and the binary code of a sample can be generated by combining the binary codes of its labels. While minimizing the Hamming distance between similar pairs and maximizing the Hamming distance between dissimilar pairs, we also learn a project matrix which can be used to generate binary codes for out-of-samples. Experimental results on three widely used data sets show that M3LH outperforms or is comparable to several state-of-the-art hashing methods." @default.
• W2565504097 created "2017-01-06" @default.
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• W2565504097 date "2016-12-31" @default.
• W2565504097 modified "2023-09-26" @default.
• W2565504097 title "M3LH: Multi-modal Multi-label Hashing for Large Scale Data Search" @default.
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• W2565504097 doi "https://doi.org/10.1007/978-3-319-51811-4_17" @default.
• W2565504097 hasPublicationYear "2016" @default.
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