A Max Margin Based Semi-Supervised Reranking Method

Tongzhe Zhang; Yuting Su; Hongbin Guo

doi:10.3788/LOP55.111001

Journals >Laser & Optoelectronics Progress >Volume 55 >Issue 11 >Page 111001 > Article

Laser & Optoelectronics Progress
Vol. 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

show less

DOI: 10.3788/LOP55.111001 Cite this Article Set citation alerts

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

show less

Fig. 1. Flow chart of the proposed reranking algorithm

Download full size

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

Download full size

Fig. 3. Performance comparison of different datasets

Download full size

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 1. Performance comparison for different trade-off parameters

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 2. Performance comparison for different labeled numbers

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 3. Performance comparison for different ranking fractional intervals

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

Table 4. Performance comparison for different image search reranking methods

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

Download Citation

Set citation alerts for the article

Tools

Set citation alerts for the article

Save the article for my favorites

Paper Information