Multiple feature kernel hashing for large-scale visual search
Recently hashing has become attractive in large-scale visual search, owing to its theoretical guarantee and practical success. However, most of the state-of-the-art hashing methods can only employ a single feature type to learn hashing functions. Related research on image search, clustering, and oth...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Liu, Xianglong [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014transfer abstract |
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| Schlagwörter: |
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| Umfang: |
10 |
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| Übergeordnetes Werk: |
Enthalten in: Association between dopa decarboxylase gene variants and borderline personality disorder - Mobascher, Arian ELSEVIER, 2014, the journal of the Pattern Recognition Society, Amsterdam |
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| Übergeordnetes Werk: |
volume:47 ; year:2014 ; number:2 ; pages:748-757 ; extent:10 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.patcog.2013.08.022 |
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| Katalog-ID: |
ELV034275282 |
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| 520 | |a Recently hashing has become attractive in large-scale visual search, owing to its theoretical guarantee and practical success. However, most of the state-of-the-art hashing methods can only employ a single feature type to learn hashing functions. Related research on image search, clustering, and other domains has proved the advantages of fusing multiple features. In this paper we propose a novel multiple feature kernel hashing framework, where hashing functions are learned to preserve certain similarities with linearly combined multiple kernels corresponding to different features. The framework is not only compatible with general types of data and diverse types of similarities indicated by different visual features, but also general for both supervised and unsupervised scenarios. We present efficient alternating optimization algorithms to learn both the hashing functions and the optimal kernel combination. Experimental results on three large-scale benchmarks CIFAR-10, NUS-WIDE and a-TRECVID show that the proposed approach can achieve superior accuracy and efficiency over state-of-the-art methods. | ||
| 520 | |a Recently hashing has become attractive in large-scale visual search, owing to its theoretical guarantee and practical success. However, most of the state-of-the-art hashing methods can only employ a single feature type to learn hashing functions. Related research on image search, clustering, and other domains has proved the advantages of fusing multiple features. In this paper we propose a novel multiple feature kernel hashing framework, where hashing functions are learned to preserve certain similarities with linearly combined multiple kernels corresponding to different features. The framework is not only compatible with general types of data and diverse types of similarities indicated by different visual features, but also general for both supervised and unsupervised scenarios. We present efficient alternating optimization algorithms to learn both the hashing functions and the optimal kernel combination. Experimental results on three large-scale benchmarks CIFAR-10, NUS-WIDE and a-TRECVID show that the proposed approach can achieve superior accuracy and efficiency over state-of-the-art methods. | ||
| 650 | 7 | |a Compact hashing |2 Elsevier | |
| 650 | 7 | |a Multiple features |2 Elsevier | |
| 650 | 7 | |a Locality-sensitive hashing |2 Elsevier | |
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| 700 | 1 | |a Lang, Bo |4 oth | |
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10.1016/j.patcog.2013.08.022 doi GBVA2014023000006.pica (DE-627)ELV034275282 (ELSEVIER)S0031-3203(13)00345-2 DE-627 ger DE-627 rakwb eng 000 150 000 DE-600 150 DE-600 Liu, Xianglong verfasserin aut Multiple feature kernel hashing for large-scale visual search 2014transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Recently hashing has become attractive in large-scale visual search, owing to its theoretical guarantee and practical success. However, most of the state-of-the-art hashing methods can only employ a single feature type to learn hashing functions. Related research on image search, clustering, and other domains has proved the advantages of fusing multiple features. In this paper we propose a novel multiple feature kernel hashing framework, where hashing functions are learned to preserve certain similarities with linearly combined multiple kernels corresponding to different features. The framework is not only compatible with general types of data and diverse types of similarities indicated by different visual features, but also general for both supervised and unsupervised scenarios. We present efficient alternating optimization algorithms to learn both the hashing functions and the optimal kernel combination. Experimental results on three large-scale benchmarks CIFAR-10, NUS-WIDE and a-TRECVID show that the proposed approach can achieve superior accuracy and efficiency over state-of-the-art methods. Recently hashing has become attractive in large-scale visual search, owing to its theoretical guarantee and practical success. However, most of the state-of-the-art hashing methods can only employ a single feature type to learn hashing functions. Related research on image search, clustering, and other domains has proved the advantages of fusing multiple features. In this paper we propose a novel multiple feature kernel hashing framework, where hashing functions are learned to preserve certain similarities with linearly combined multiple kernels corresponding to different features. The framework is not only compatible with general types of data and diverse types of similarities indicated by different visual features, but also general for both supervised and unsupervised scenarios. We present efficient alternating optimization algorithms to learn both the hashing functions and the optimal kernel combination. Experimental results on three large-scale benchmarks CIFAR-10, NUS-WIDE and a-TRECVID show that the proposed approach can achieve superior accuracy and efficiency over state-of-the-art methods. Compact hashing Elsevier Multiple features Elsevier Locality-sensitive hashing Elsevier Multiple kernels Elsevier He, Junfeng oth Lang, Bo oth Enthalten in Elsevier Mobascher, Arian ELSEVIER Association between dopa decarboxylase gene variants and borderline personality disorder 2014 the journal of the Pattern Recognition Society Amsterdam (DE-627)ELV017326583 volume:47 year:2014 number:2 pages:748-757 extent:10 https://doi.org/10.1016/j.patcog.2013.08.022 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 47 2014 2 748-757 10 045F 000 |
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10.1016/j.patcog.2013.08.022 doi GBVA2014023000006.pica (DE-627)ELV034275282 (ELSEVIER)S0031-3203(13)00345-2 DE-627 ger DE-627 rakwb eng 000 150 000 DE-600 150 DE-600 Liu, Xianglong verfasserin aut Multiple feature kernel hashing for large-scale visual search 2014transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Recently hashing has become attractive in large-scale visual search, owing to its theoretical guarantee and practical success. However, most of the state-of-the-art hashing methods can only employ a single feature type to learn hashing functions. Related research on image search, clustering, and other domains has proved the advantages of fusing multiple features. In this paper we propose a novel multiple feature kernel hashing framework, where hashing functions are learned to preserve certain similarities with linearly combined multiple kernels corresponding to different features. The framework is not only compatible with general types of data and diverse types of similarities indicated by different visual features, but also general for both supervised and unsupervised scenarios. We present efficient alternating optimization algorithms to learn both the hashing functions and the optimal kernel combination. Experimental results on three large-scale benchmarks CIFAR-10, NUS-WIDE and a-TRECVID show that the proposed approach can achieve superior accuracy and efficiency over state-of-the-art methods. Recently hashing has become attractive in large-scale visual search, owing to its theoretical guarantee and practical success. However, most of the state-of-the-art hashing methods can only employ a single feature type to learn hashing functions. Related research on image search, clustering, and other domains has proved the advantages of fusing multiple features. In this paper we propose a novel multiple feature kernel hashing framework, where hashing functions are learned to preserve certain similarities with linearly combined multiple kernels corresponding to different features. The framework is not only compatible with general types of data and diverse types of similarities indicated by different visual features, but also general for both supervised and unsupervised scenarios. We present efficient alternating optimization algorithms to learn both the hashing functions and the optimal kernel combination. Experimental results on three large-scale benchmarks CIFAR-10, NUS-WIDE and a-TRECVID show that the proposed approach can achieve superior accuracy and efficiency over state-of-the-art methods. Compact hashing Elsevier Multiple features Elsevier Locality-sensitive hashing Elsevier Multiple kernels Elsevier He, Junfeng oth Lang, Bo oth Enthalten in Elsevier Mobascher, Arian ELSEVIER Association between dopa decarboxylase gene variants and borderline personality disorder 2014 the journal of the Pattern Recognition Society Amsterdam (DE-627)ELV017326583 volume:47 year:2014 number:2 pages:748-757 extent:10 https://doi.org/10.1016/j.patcog.2013.08.022 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 47 2014 2 748-757 10 045F 000 |
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10.1016/j.patcog.2013.08.022 doi GBVA2014023000006.pica (DE-627)ELV034275282 (ELSEVIER)S0031-3203(13)00345-2 DE-627 ger DE-627 rakwb eng 000 150 000 DE-600 150 DE-600 Liu, Xianglong verfasserin aut Multiple feature kernel hashing for large-scale visual search 2014transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Recently hashing has become attractive in large-scale visual search, owing to its theoretical guarantee and practical success. However, most of the state-of-the-art hashing methods can only employ a single feature type to learn hashing functions. Related research on image search, clustering, and other domains has proved the advantages of fusing multiple features. In this paper we propose a novel multiple feature kernel hashing framework, where hashing functions are learned to preserve certain similarities with linearly combined multiple kernels corresponding to different features. The framework is not only compatible with general types of data and diverse types of similarities indicated by different visual features, but also general for both supervised and unsupervised scenarios. We present efficient alternating optimization algorithms to learn both the hashing functions and the optimal kernel combination. Experimental results on three large-scale benchmarks CIFAR-10, NUS-WIDE and a-TRECVID show that the proposed approach can achieve superior accuracy and efficiency over state-of-the-art methods. Recently hashing has become attractive in large-scale visual search, owing to its theoretical guarantee and practical success. However, most of the state-of-the-art hashing methods can only employ a single feature type to learn hashing functions. Related research on image search, clustering, and other domains has proved the advantages of fusing multiple features. In this paper we propose a novel multiple feature kernel hashing framework, where hashing functions are learned to preserve certain similarities with linearly combined multiple kernels corresponding to different features. The framework is not only compatible with general types of data and diverse types of similarities indicated by different visual features, but also general for both supervised and unsupervised scenarios. We present efficient alternating optimization algorithms to learn both the hashing functions and the optimal kernel combination. Experimental results on three large-scale benchmarks CIFAR-10, NUS-WIDE and a-TRECVID show that the proposed approach can achieve superior accuracy and efficiency over state-of-the-art methods. Compact hashing Elsevier Multiple features Elsevier Locality-sensitive hashing Elsevier Multiple kernels Elsevier He, Junfeng oth Lang, Bo oth Enthalten in Elsevier Mobascher, Arian ELSEVIER Association between dopa decarboxylase gene variants and borderline personality disorder 2014 the journal of the Pattern Recognition Society Amsterdam (DE-627)ELV017326583 volume:47 year:2014 number:2 pages:748-757 extent:10 https://doi.org/10.1016/j.patcog.2013.08.022 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 47 2014 2 748-757 10 045F 000 |
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10.1016/j.patcog.2013.08.022 doi GBVA2014023000006.pica (DE-627)ELV034275282 (ELSEVIER)S0031-3203(13)00345-2 DE-627 ger DE-627 rakwb eng 000 150 000 DE-600 150 DE-600 Liu, Xianglong verfasserin aut Multiple feature kernel hashing for large-scale visual search 2014transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Recently hashing has become attractive in large-scale visual search, owing to its theoretical guarantee and practical success. However, most of the state-of-the-art hashing methods can only employ a single feature type to learn hashing functions. Related research on image search, clustering, and other domains has proved the advantages of fusing multiple features. In this paper we propose a novel multiple feature kernel hashing framework, where hashing functions are learned to preserve certain similarities with linearly combined multiple kernels corresponding to different features. The framework is not only compatible with general types of data and diverse types of similarities indicated by different visual features, but also general for both supervised and unsupervised scenarios. We present efficient alternating optimization algorithms to learn both the hashing functions and the optimal kernel combination. Experimental results on three large-scale benchmarks CIFAR-10, NUS-WIDE and a-TRECVID show that the proposed approach can achieve superior accuracy and efficiency over state-of-the-art methods. Recently hashing has become attractive in large-scale visual search, owing to its theoretical guarantee and practical success. However, most of the state-of-the-art hashing methods can only employ a single feature type to learn hashing functions. Related research on image search, clustering, and other domains has proved the advantages of fusing multiple features. In this paper we propose a novel multiple feature kernel hashing framework, where hashing functions are learned to preserve certain similarities with linearly combined multiple kernels corresponding to different features. The framework is not only compatible with general types of data and diverse types of similarities indicated by different visual features, but also general for both supervised and unsupervised scenarios. We present efficient alternating optimization algorithms to learn both the hashing functions and the optimal kernel combination. Experimental results on three large-scale benchmarks CIFAR-10, NUS-WIDE and a-TRECVID show that the proposed approach can achieve superior accuracy and efficiency over state-of-the-art methods. Compact hashing Elsevier Multiple features Elsevier Locality-sensitive hashing Elsevier Multiple kernels Elsevier He, Junfeng oth Lang, Bo oth Enthalten in Elsevier Mobascher, Arian ELSEVIER Association between dopa decarboxylase gene variants and borderline personality disorder 2014 the journal of the Pattern Recognition Society Amsterdam (DE-627)ELV017326583 volume:47 year:2014 number:2 pages:748-757 extent:10 https://doi.org/10.1016/j.patcog.2013.08.022 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 47 2014 2 748-757 10 045F 000 |
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10.1016/j.patcog.2013.08.022 doi GBVA2014023000006.pica (DE-627)ELV034275282 (ELSEVIER)S0031-3203(13)00345-2 DE-627 ger DE-627 rakwb eng 000 150 000 DE-600 150 DE-600 Liu, Xianglong verfasserin aut Multiple feature kernel hashing for large-scale visual search 2014transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Recently hashing has become attractive in large-scale visual search, owing to its theoretical guarantee and practical success. However, most of the state-of-the-art hashing methods can only employ a single feature type to learn hashing functions. Related research on image search, clustering, and other domains has proved the advantages of fusing multiple features. In this paper we propose a novel multiple feature kernel hashing framework, where hashing functions are learned to preserve certain similarities with linearly combined multiple kernels corresponding to different features. The framework is not only compatible with general types of data and diverse types of similarities indicated by different visual features, but also general for both supervised and unsupervised scenarios. We present efficient alternating optimization algorithms to learn both the hashing functions and the optimal kernel combination. Experimental results on three large-scale benchmarks CIFAR-10, NUS-WIDE and a-TRECVID show that the proposed approach can achieve superior accuracy and efficiency over state-of-the-art methods. Recently hashing has become attractive in large-scale visual search, owing to its theoretical guarantee and practical success. However, most of the state-of-the-art hashing methods can only employ a single feature type to learn hashing functions. Related research on image search, clustering, and other domains has proved the advantages of fusing multiple features. In this paper we propose a novel multiple feature kernel hashing framework, where hashing functions are learned to preserve certain similarities with linearly combined multiple kernels corresponding to different features. The framework is not only compatible with general types of data and diverse types of similarities indicated by different visual features, but also general for both supervised and unsupervised scenarios. We present efficient alternating optimization algorithms to learn both the hashing functions and the optimal kernel combination. Experimental results on three large-scale benchmarks CIFAR-10, NUS-WIDE and a-TRECVID show that the proposed approach can achieve superior accuracy and efficiency over state-of-the-art methods. Compact hashing Elsevier Multiple features Elsevier Locality-sensitive hashing Elsevier Multiple kernels Elsevier He, Junfeng oth Lang, Bo oth Enthalten in Elsevier Mobascher, Arian ELSEVIER Association between dopa decarboxylase gene variants and borderline personality disorder 2014 the journal of the Pattern Recognition Society Amsterdam (DE-627)ELV017326583 volume:47 year:2014 number:2 pages:748-757 extent:10 https://doi.org/10.1016/j.patcog.2013.08.022 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 47 2014 2 748-757 10 045F 000 |
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Association between dopa decarboxylase gene variants and borderline personality disorder |
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ELV017326583 |
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Association between dopa decarboxylase gene variants and borderline personality disorder |
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Multiple feature kernel hashing for large-scale visual search |
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Multiple feature kernel hashing for large-scale visual search |
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Liu, Xianglong |
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Association between dopa decarboxylase gene variants and borderline personality disorder |
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Association between dopa decarboxylase gene variants and borderline personality disorder |
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eng |
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Liu, Xianglong |
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10.1016/j.patcog.2013.08.022 |
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000 150 |
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multiple feature kernel hashing for large-scale visual search |
| title_auth |
Multiple feature kernel hashing for large-scale visual search |
| abstract |
Recently hashing has become attractive in large-scale visual search, owing to its theoretical guarantee and practical success. However, most of the state-of-the-art hashing methods can only employ a single feature type to learn hashing functions. Related research on image search, clustering, and other domains has proved the advantages of fusing multiple features. In this paper we propose a novel multiple feature kernel hashing framework, where hashing functions are learned to preserve certain similarities with linearly combined multiple kernels corresponding to different features. The framework is not only compatible with general types of data and diverse types of similarities indicated by different visual features, but also general for both supervised and unsupervised scenarios. We present efficient alternating optimization algorithms to learn both the hashing functions and the optimal kernel combination. Experimental results on three large-scale benchmarks CIFAR-10, NUS-WIDE and a-TRECVID show that the proposed approach can achieve superior accuracy and efficiency over state-of-the-art methods. |
| abstractGer |
Recently hashing has become attractive in large-scale visual search, owing to its theoretical guarantee and practical success. However, most of the state-of-the-art hashing methods can only employ a single feature type to learn hashing functions. Related research on image search, clustering, and other domains has proved the advantages of fusing multiple features. In this paper we propose a novel multiple feature kernel hashing framework, where hashing functions are learned to preserve certain similarities with linearly combined multiple kernels corresponding to different features. The framework is not only compatible with general types of data and diverse types of similarities indicated by different visual features, but also general for both supervised and unsupervised scenarios. We present efficient alternating optimization algorithms to learn both the hashing functions and the optimal kernel combination. Experimental results on three large-scale benchmarks CIFAR-10, NUS-WIDE and a-TRECVID show that the proposed approach can achieve superior accuracy and efficiency over state-of-the-art methods. |
| abstract_unstemmed |
Recently hashing has become attractive in large-scale visual search, owing to its theoretical guarantee and practical success. However, most of the state-of-the-art hashing methods can only employ a single feature type to learn hashing functions. Related research on image search, clustering, and other domains has proved the advantages of fusing multiple features. In this paper we propose a novel multiple feature kernel hashing framework, where hashing functions are learned to preserve certain similarities with linearly combined multiple kernels corresponding to different features. The framework is not only compatible with general types of data and diverse types of similarities indicated by different visual features, but also general for both supervised and unsupervised scenarios. We present efficient alternating optimization algorithms to learn both the hashing functions and the optimal kernel combination. Experimental results on three large-scale benchmarks CIFAR-10, NUS-WIDE and a-TRECVID show that the proposed approach can achieve superior accuracy and efficiency over state-of-the-art methods. |
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Multiple feature kernel hashing for large-scale visual search |
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https://doi.org/10.1016/j.patcog.2013.08.022 |
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He, Junfeng Lang, Bo |
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2024-07-06T20:42:46.658Z |
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7.401513 |