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Albert Gordo, Florent Perronnin, Yunchao Gong, & Svetlana Lazebnik. (2014). Asymmetric Distances for Binary Embeddings. TPAMI - IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(1), 33–47.
Abstract: In large-scale query-by-example retrieval, embedding image signatures in a binary space offers two benefits: data compression and search efficiency. While most embedding algorithms binarize both query and database signatures, it has been noted that this is not strictly a requirement. Indeed, asymmetric schemes which binarize the database signatures but not the query still enjoy the same two benefits but may provide superior accuracy. In this work, we propose two general asymmetric distances which are applicable to a wide variety of embedding techniques including Locality Sensitive Hashing (LSH), Locality Sensitive Binary Codes (LSBC), Spectral Hashing (SH), PCA Embedding (PCAE), PCA Embedding with random rotations (PCAE-RR), and PCA Embedding with iterative quantization (PCAE-ITQ). We experiment on four public benchmarks containing up to 1M images and show that the proposed asymmetric distances consistently lead to large improvements over the symmetric Hamming distance for all binary embedding techniques.
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Josep Llados, & Dorothea Blostein. (2007). Special Issue on Graphics Recognition. IJDAR - International Journal on Document Analysis and Recognition, 1–2.
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Ernest Valveny, Philippe Dosch, Adam Winstanley, Yu Zhou, Su Yang, Luo Yan, et al. (2006). A general framework for the evaluation of symbol recognition methods. International Journal on Document Analysis and Recognition (IJDAR), 9(1): 59–74.
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Gemma Sanchez, Alicia Fornes, Joan Mas, & Josep Llados. (2007). Computer Vision Tools for Visually Impaired Children Learning.
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Gemma Sanchez, Alicia Fornes, Joan Mas, & Josep Llados. (2007). Computer Vision Tools for Visually Impaired Children Learning.
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