Researching | A Max Margin Based Semi-Supervised Reranking Method

Laser & Optoelectronics Progress, Volume. 55, Issue 11, 111001(2018)

A Max Margin Based Semi-Supervised Reranking Method

Tongzhe Zhang, Yuting Su, and Hongbin Guo^*

Author Affiliations

School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China

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Figures&Tables (7)

References (22)

Paper Information

Figures & Tables(7)

Fig. 1. Flow chart of the proposed reranking algorithm

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Fig. 2. Performance comparison between (a) initial search results and (b) reranking results based on query "angel"

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Fig. 3. Performance comparison of different datasets

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Table 1. Performance comparison for different trade-off parameters

Table 1. Performance comparison for different trade-off parameters

C	Depth
C	10	20	30	40	50	60	70	80	90	100
0.01	0.634	0.590	0.572	0.563	0.559	0.557	0.555	0.555	0.558	0.562
0.1	0.807	0.722	0.682	0.659	0.644	0.634	0.628	0.625	0.624	0.626
1	0.862	0.766	0.723	0.697	0.679	0.667	0.659	0.656	0.653	0.653
10	0.861	0.766	0.722	0.695	0.676	0.666	0.658	0.655	0.652	0.652
100	0.861	0.766	0.722	0.695	0.676	0.666	0.658	0.654	0.652	0.652

Table 2. Performance comparison for different labeled numbers

Table 2. Performance comparison for different labeled numbers

k	Depth
k	10	20	30	40	50	60	70	80	90	100
5	0.862	0.766	0.723	0.697	0.679	0.667	0.659	0.656	0.653	0.653
10	0.930	0.844	0.783	0.747	0.728	0.714	0.704	0.698	0.694	0.693
15	0.866	0.879	0.827	0.789	0.762	0.744	0.731	0.723	0.717	0.715
20	0.803	0.851	0.847	0.812	0.786	0.766	0.751	0.742	0.735	0.730

Table 3. Performance comparison for different ranking fractional intervals

Table 3. Performance comparison for different ranking fractional intervals

m	Depth
m	10	20	30	40	50	60	70	80	90	100
0	0.769	0.695	0.661	0.640	0.626	0.618	0.613	0.610	0.610	0.614
0.5	0.862	0.766	0.723	0.697	0.679	0.667	0.659	0.656	0.653	0.653
1	0.856	0.764	0.722	0.697	0.679	0.666	0.659	0.655	0.653	0.653
1.5	0.850	0.761	0.719	0.692	0.677	0.665	0.656	0.652	0.650	0.650
2	0.844	0.756	0.715	0.686	0.671	0.661	0.654	0.650	0.647	0.647
2.5	0.838	0.752	0.711	0.683	0.666	0.657	0.650	0.647	0.644	0.643
3	0.832	0.746	0.705	0.680	0.661	0.653	0.646	0.642	0.639	0.640
3.5	0.827	0.740	0.699	0.674	0.657	0.648	0.641	0.637	0.635	0.637
4	0.823	0.736	0.695	0.670	0.654	0.643	0.637	0.633	0.631	0.634
4.5	0.815	0.729	0.688	0.663	0.648	0.638	0.632	0.628	0.627	0.630
5	0.807	0.722	0.682	0.659	0.644	0.634	0.628	0.625	0.624	0.626

Table 4. Performance comparison for different image search reranking methods

Table 4. Performance comparison for different image search reranking methods

Method	Depth
Method	10	20	30	40	50	60	70	80	90	100
RankSVM	0.670	0.665	0.659	0.649	0.641	0.636	0.634	0.634	0.633	0.636
RankSVM+LPP	0.801	0.735	0.702	0.679	0.669	0.659	0.654	0.651	0.649	0.650
RANGE	0.835	0.753	0.717	0.692	0.676	0.666	0.660	0.658	0.656	0.657
Proposed	0.859	0.760	0.719	0.691	0.676	0.668	0.662	0.659	0.658	0.658

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Tongzhe Zhang, Yuting Su, Hongbin Guo. A Max Margin Based Semi-Supervised Reranking Method[J]. Laser & Optoelectronics Progress, 2018, 55(11): 111001

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Paper Information

Category: Image Processing

Received: Mar. 23, 2018

Accepted: May. 28, 2018

Published Online: Aug. 14, 2019

The Author Email: Hongbin Guo (ghb3011204117@163.com)

DOI:10.3788/LOP55.111001

Topics

laser devices and laser physics

Lasers and Laser Optics

laser manufacturing

Instrumentation, Measurement and Metrology