Tut MUVIS image retrieval system proposal for MSR-Bing challenge 2014

J. Raitoharju; H. Zhang; E. C. Ozan; M. A. Waris; M. Faisal; G. Cao; M. Roininen; I. Ahmad; R. Shetty; S. Uhlmann; K. Samiee; S. Kiranyaz; M. Gabbouj

doi:10.1109/ICMEW.2014.6890600

Tut MUVIS image retrieval system proposal for MSR-Bing challenge 2014

Julkaisun otsikon käännös: TUT Muvis Image Retrieval System Proposal for MSR-BING Challenge 2014

J. Raitoharju, H. Zhang, E. C. Ozan, M. A. Waris, M. Faisal, G. Cao, M. Roininen, I. Ahmad, R. Shetty, S. Uhlmann, K. Samiee, S. Kiranyaz, M. Gabbouj

Tutkimustuotos: Konferenssiartikkeli › Tieteellinen › vertaisarvioitu

2 Sitaatiot (Scopus)

Abstrakti

This paper presents our system designed for MSR-Bing Image Retrieval Challenge @ ICME 2014. The core of our system is formed by a text processing module combined with a module performing PCA-assisted perceptron regression with random sub-space selection (P²R²S²). P²R²S² uses Over-Feat features as a starting point and transforms them into more descriptive features via unsupervised training. The relevance score for each query-image pair is obtained by comparing the transformed features of the query image and the relevant training images. We also use a face bank, duplicate image detection, and optical character recognition to boost our evaluation accuracy. Our system achieves 0.5099 in terms of DCG₂₅ on the development set and 0.5116 on the test set.

Julkaisun otsikon käännös	TUT Muvis Image Retrieval System Proposal for MSR-BING Challenge 2014
Alkuperäiskieli	Englanti
Otsikko	IEEE International Conference on Multimedia and Expo, ICME 2014, Chengdu, China, July 14-18, 2014
Julkaisupaikka	Piscataway
Kustantaja	IEEE
Sivut	1-6
Sivumäärä	6
ISBN (painettu)	9781479947171
DOI - pysyväislinkit	https://doi.org/10.1109/ICMEW.2014.6890600
Tila	Julkaistu - 3 syysk. 2014
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO - Kesto: 1 tammik. 1900 → …

Conference

Conference	IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO
Ajanjakso	1/01/00 → …

Julkaisufoorumi-taso

Jufo-taso 1

!!ASJC Scopus subject areas

Computer Graphics and Computer-Aided Design
Computer Vision and Pattern Recognition
Human-Computer Interaction

Pääsy asiakirjaan

10.1109/ICMEW.2014.6890600

Muut tiedostot ja linkit

Link to publication in Scopus

Siteeraa tätä

Raitoharju, J., Zhang, H., Ozan, E. C., Waris, M. A., Faisal, M., Cao, G., Roininen, M., Ahmad, I., Shetty, R., Uhlmann, S., Samiee, K., Kiranyaz, S., & Gabbouj, M. (2014). Tut MUVIS image retrieval system proposal for MSR-Bing challenge 2014. teoksessa IEEE International Conference on Multimedia and Expo, ICME 2014, Chengdu, China, July 14-18, 2014 (Sivut 1-6). IEEE. https://doi.org/10.1109/ICMEW.2014.6890600

@inproceedings{511dfcef63e941bf9bcf4641fdddf7c2,

title = "Tut MUVIS image retrieval system proposal for MSR-Bing challenge 2014",

abstract = "This paper presents our system designed for MSR-Bing Image Retrieval Challenge @ ICME 2014. The core of our system is formed by a text processing module combined with a module performing PCA-assisted perceptron regression with random sub-space selection (P2R2S2). P2R2S2 uses Over-Feat features as a starting point and transforms them into more descriptive features via unsupervised training. The relevance score for each query-image pair is obtained by comparing the transformed features of the query image and the relevant training images. We also use a face bank, duplicate image detection, and optical character recognition to boost our evaluation accuracy. Our system achieves 0.5099 in terms of DCG25 on the development set and 0.5116 on the test set.",

keywords = "Data Partitioning, Face Bank, Image Retrieval, Relevance Evaluation",

author = "J. Raitoharju and H. Zhang and Ozan, {E. C.} and Waris, {M. A.} and M. Faisal and G. Cao and M. Roininen and I. Ahmad and R. Shetty and S. Uhlmann and K. Samiee and S. Kiranyaz and M. Gabbouj",

note = "Contribution: organisation=sgn,FACT1=1<br/>Portfolio EDEND: 2014-09-25; IEEE International Conference on Multimedia and Expo ; Conference date: 01-01-1900",

year = "2014",

month = sep,

day = "3",

doi = "10.1109/ICMEW.2014.6890600",

language = "English",

isbn = "9781479947171",

pages = "1--6",

booktitle = "IEEE International Conference on Multimedia and Expo, ICME 2014, Chengdu, China, July 14-18, 2014",

publisher = "IEEE",

address = "United States",

}

Raitoharju, J, Zhang, H, Ozan, EC, Waris, MA, Faisal, M, Cao, G, Roininen, M, Ahmad, I, Shetty, R, Uhlmann, S, Samiee, K, Kiranyaz, S & Gabbouj, M 2014, Tut MUVIS image retrieval system proposal for MSR-Bing challenge 2014. julkaisussa IEEE International Conference on Multimedia and Expo, ICME 2014, Chengdu, China, July 14-18, 2014. IEEE, Piscataway, Sivut 1-6, IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO, 1/01/00. https://doi.org/10.1109/ICMEW.2014.6890600

TY - GEN

T1 - Tut MUVIS image retrieval system proposal for MSR-Bing challenge 2014

AU - Raitoharju, J.

AU - Zhang, H.

AU - Ozan, E. C.

AU - Waris, M. A.

AU - Faisal, M.

AU - Cao, G.

AU - Roininen, M.

AU - Ahmad, I.

AU - Shetty, R.

AU - Uhlmann, S.

AU - Samiee, K.

AU - Kiranyaz, S.

AU - Gabbouj, M.

N1 - Contribution: organisation=sgn,FACT1=1<br/>Portfolio EDEND: 2014-09-25

PY - 2014/9/3

Y1 - 2014/9/3

N2 - This paper presents our system designed for MSR-Bing Image Retrieval Challenge @ ICME 2014. The core of our system is formed by a text processing module combined with a module performing PCA-assisted perceptron regression with random sub-space selection (P2R2S2). P2R2S2 uses Over-Feat features as a starting point and transforms them into more descriptive features via unsupervised training. The relevance score for each query-image pair is obtained by comparing the transformed features of the query image and the relevant training images. We also use a face bank, duplicate image detection, and optical character recognition to boost our evaluation accuracy. Our system achieves 0.5099 in terms of DCG25 on the development set and 0.5116 on the test set.

AB - This paper presents our system designed for MSR-Bing Image Retrieval Challenge @ ICME 2014. The core of our system is formed by a text processing module combined with a module performing PCA-assisted perceptron regression with random sub-space selection (P2R2S2). P2R2S2 uses Over-Feat features as a starting point and transforms them into more descriptive features via unsupervised training. The relevance score for each query-image pair is obtained by comparing the transformed features of the query image and the relevant training images. We also use a face bank, duplicate image detection, and optical character recognition to boost our evaluation accuracy. Our system achieves 0.5099 in terms of DCG25 on the development set and 0.5116 on the test set.

KW - Data Partitioning

KW - Face Bank

KW - Image Retrieval

KW - Relevance Evaluation

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U2 - 10.1109/ICMEW.2014.6890600

DO - 10.1109/ICMEW.2014.6890600

M3 - Conference contribution

SN - 9781479947171

SP - 1

EP - 6

BT - IEEE International Conference on Multimedia and Expo, ICME 2014, Chengdu, China, July 14-18, 2014

PB - IEEE

CY - Piscataway

T2 - IEEE International Conference on Multimedia and Expo

Y2 - 1 January 1900

ER -

Tut MUVIS image retrieval system proposal for MSR-Bing challenge 2014

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!!ASJC Scopus subject areas

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