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Table 1 Recall and precision of each feature detector/descriptor combination. The test run on the Oxford dataset and results in the 4th through 6th columns are for each image on average. For reference, the average raw image volume is 574.2 KB. Assume that a float type number takes 4 Bytes in storage. (The number in the parentheses in columns 4, 5, and 6 indicates the "rank'' of the detector/descriptor combination in terms of the corresponding performance metric in that column. The "rank-based score'' in the last column is the summation of the three ranks along the row.)

From: Feature-Based Image Comparison for Semantic Neighbor Selection in Resource-Constrained Visual Sensor Networks

 

Descr. length

No. of feature points

Feature data vol. (KB)

Recall

Precision

Rank-based score

SIFT

128

5938

3040.3()

0.3542()

1.0000()

21

SURF

64

1478

378.4()

0.3958()

1.0000()

17

Harris + SIFT descr.

128

442

226.3()

0.4583()

0.9375()

13

Harris + SURF descr.

64

442

113.2()

0.7083()

0.6970()

12

DWT + SIFT descr.

128

1145

586.2()

0.4375()

0.9063()

18

DWT + SURF descr.

64

1145

293.1()

0.4375()

0.5758()

18

SIFT detec.+ M. I.

7

5938

166.3()

0.4167()

0.5084()

19

SURF detec.+ M. I.

7

1478

41.4()

0.3958()

0.4029()

19

Harris + M. I.

7

442

12.4()

0.7292()

0.2014()

12

DWT + M.I.

7

1145

32.1()

0.8958()

0.1566()

13