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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
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초록· 키워드
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CBIR is an effective tool to search and extract image contents in a large remote sensing image database queried by an operator or end user. However, as imaging principles are different by sensors, their visual representation thus varies among image modality type. Considering images of various modalities archived in the database, image modality difference has to be tackled for the successful CBIR implementation. However, this topic has been seldom dealt with and thus still poses a practical challenge. This study suggests a cross modality CBIR (termed as the CM-CBIR) method that transforms given query feature vector by a supervised procedure in order to link between modalities. This procedure leverages the skill of analyst in training steps after which the transformed query vector is created for the use of searching in target images with different modalities. Current initial results show the potential of the proposed CM-CBIR method by delivering the image content of interest from different modality images. Despite its retrieval capability is outperformed by that of same modality CBIR (abbreviated as the SM-CBIR), the lack of retrieval performance can be compensated by employing the user’s relevancy feedback, a conventional technique for retrieval enhancement.
목차
Abstract
1. Introduction
2. Methods and approach
3. Experimental results and discussion
4. Conclusions
References
참고문헌 (9)
참고문헌 신청
1. [학술대회논문] - Brocker, L. / 2001 / Improving the retrieval performance of content-based image retrieval systems: The GIVBAC approach / Fifth International Conference on Information Visualisation : 659 ~ 664
2. [보고서] - Gelasca, E. D. / 2007 / CORTINA: Searching a 10 Million + Images Database
3. [학술지(정기간행물)] - 정인성 / 2012 / An approach for improving the performance of the Content-Based Image Retrieval (CBIR) / 한국측량학회지 30 (6) : 665 ~ 672
4. [학술대회논문] - Jia, Y. / 2011 / Learning crossmodality similarity for multinomial data / 2011 IEEE International Conference on Computer Vision (ICCV) : 2407 ~ 2414
5. [학술지(정기간행물)] - Li, J. / 2004 / Integrated spectral and spatial information mining in remote sensing imagery / IEEE Transactionson Geoscience and Remote Sensing 42 (3) : 673 ~ 685
2. [보고서] - Gelasca, E. D. / 2007 / CORTINA: Searching a 10 Million + Images Database
3. [학술지(정기간행물)] - 정인성 / 2012 / An approach for improving the performance of the Content-Based Image Retrieval (CBIR) / 한국측량학회지 30 (6) : 665 ~ 672
4. [학술대회논문] - Jia, Y. / 2011 / Learning crossmodality similarity for multinomial data / 2011 IEEE International Conference on Computer Vision (ICCV) : 2407 ~ 2414
5. [학술지(정기간행물)] - Li, J. / 2004 / Integrated spectral and spatial information mining in remote sensing imagery / IEEE Transactionson Geoscience and Remote Sensing 42 (3) : 673 ~ 685
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UCI(KEPA) : I410-ECN-0101-2015-530-001377362