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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