Sum versus vote fusion in multiple classifier systems

Kittler, Josef; Alkoot, Fuad M.

doi:10.1109/tpami.2003.1159950

Cited by 200 publications

(102 citation statements)

References 14 publications

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“…Jain and Ross [35] describe an interesting approach to the realization of a multimodal biometric verification system based on face, fingerprint, and hand-geometry features that uses fusion at the matchingscore level based on learning user-specific matching thresholds as well as the weights of an individual biometric trait. Some other references related to fusion at the matchingscore and decision levels are [36], [37], [38], [39]. Fig.…”

Section: Related Workmentioning

confidence: 99%

A biometric identification system based on eigenpalm and eigenfinger features

Ribarić

Fratrić

2005

IEEE Trans. Pattern Anal. Machine Intell.

306

180

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Abstract-This paper presents a multimodal biometric identification system based on the features of the human hand. We describe a new biometric approach to personal identification using eigenfinger and eigenpalm features, with fusion applied at the matching-score level. The identification process can be divided into the following phases: capturing the image; preprocessing; extracting and normalizing the palm and strip-like finger subimages; extracting the eigenpalm and eigenfinger features based on the K-L transform; matching and fusion; and, finally, a decision based on the (k, l)-NN classifier and thresholding. The system was tested on a database of 237 people (1,820 hand images). The experimental results showed the effectiveness of the system in terms of the recognition rate (100 percent), the equal error rate (EER = 0.58 percent), and the total error rate (TER = 0.72 percent).

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Section: Related Workmentioning

confidence: 99%

A biometric identification system based on eigenpalm and eigenfinger features

Ribarić

Fratrić

2005

IEEE Trans. Pattern Anal. Machine Intell.

306

180

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“…This challenging goal has yet to be achieved. However, combining several individual methods, based on consensus methods used in other fields [16][17][18][19][20] may improve the delineation of lesions on PET images. Not only would improvements in segmentation accuracy and robustness aid target delineation for radiation therapy, they may offer improvements in determining treatment response.…”

mentioning

confidence: 99%

Combining multiple FDG‐PET radiotherapy target segmentation methods to reduce the effect of variable performance of individual segmentation methods

et al. 2013

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Purpose: Many approaches have been proposed to segment high uptake objects in 18F-fluoro-deoxyglucose positron emission tomography images but none provides consistent performance across the large variety of imaging situations. This study investigates the use of two methods of combining individual segmentation methods to reduce the impact of inconsistent performance of the individual methods: simple majority voting and probabilistic estimation. Methods: The National Electrical Manufacturers Association image quality phantom containing five glass spheres with diameters 13-37 mm and two irregularly shaped volumes (16 and 32 cc) formed by deforming high-density polyethylene bottles in a hot water bath were filled with 18-fluorodeoxyglucose and iodine contrast agent. Repeated 5-min positron emission tomography (PET) images were acquired at 4:1 and 8:1 object-to-background contrasts for spherical objects and 4.5:1 and 9:1 for irregular objects. Five individual methods were used to segment each object: 40% thresholding, adaptive thresholding, k-means clustering, seeded region-growing, and a gradient based method. Volumes were combined using a majority vote (MJV) or Simultaneous Truth And Performance Level Estimate (STAPLE) method. Accuracy of segmentations relative to CT ground truth volumes were assessed using the Dice similarity coefficient (DSC) and the symmetric mean absolute surface distances (SMASDs). Results: MJV had median DSC values of 0.886 and 0.875; and SMASD of 0.52 and 0.71 mm for spheres and irregular shapes, respectively. STAPLE provided similar results with median DSC of 0.886 and 0.871; and median SMASD of 0.50 and 0.72 mm for spheres and irregular shapes, respectively. STAPLE had significantly higher DSC and lower SMASD values than MJV for spheres (DSC, p < 0.0001; SMASD, p = 0.0101) but MJV had significantly higher DSC and lower SMASD values compared to STAPLE for irregular shapes (DSC, p < 0.0001; SMASD, p = 0.0027). DSC was not significantly different between 128 × 128 and 256 × 256 grid sizes for either method (MJV, p = 0.0519; STAPLE, p = 0.5672) but was for SMASD values (MJV, p < 0.0001; STAPLE, p = 0.0164). The best individual method varied depending on object characteristics. However, both MJV and STAPLE provided essentially equivalent accuracy to using the best independent method in every situation, with mean differences in DSC of 0.01-0.03, and 0.05-0.12 mm for SMASD. Conclusions: Combining segmentations offers a robust approach to object segmentation in PET. Both MJV and STAPLE improved accuracy and were robust against the widely varying performance of individual segmentation methods. Differences between MJV and STAPLE are such that either offers good performance when combining volumes. Neither method requires a training dataset but MJV is simpler to interpret, easy to implement and fast.

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“…Previous work in CFA ( [6] [7], [10]- [11], [13], [16], [23], [28] When plotting the graph of the rank/score function (hence it is called the rank/score graph) of scoring systems A and B on the same coordinate plane, the diversity measure can be easily visualized. Different diversity measurements have been considered in other application domains ( [2], [5]- [7], [10][12], [13], [16], [23], [28]).…”

Section: Remarkmentioning

confidence: 99%

“…Different diversity measurements have been considered in other application domains ( [2], [5]- [7], [10][12], [13], [16], [23], [28]). …”

Section: Remarkmentioning

confidence: 99%