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SIFT10M Data Set
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Abstract: In SIFT10M, each data point is a SIFT feature which is extracted from Caltech-256 by the open source VLFeat library. The corresponding patches of the SIFT features are provided.

Data Set Characteristics:  

Multivariate

Number of Instances:

11164866

Area:

Computer

Attribute Characteristics:

Integer

Number of Attributes:

128

Date Donated

2016-02-23

Associated Tasks:

Causal-Discovery

Missing Values?

N/A

Number of Web Hits:

25760

Source:

Xiping Fu, Brendan McCane, Steven Mills, Michael Albert and Lech Szymanski
Department of Computer Science, University of Otago, Dunedin, New Zealand
{xiping, mccane, steven, malbert, lechszym}@cs.otago.ac.nz


Data Set Information:

In SIFT10M, the titles of the png files indicate the columns position of the SIFT features. This data set has been used for evaluating the approximate nearest neighbour search methods. The patches can be used for visualisation purpose and helps for analysing the performance of the corresponding approximate nearest neighbour search methods.


Attribute Information:

Each SIFT feature is a 128D column, and the corresponding patch is saved in 41*41 png format. The png files are compressed into 307 tar files for downloading.


Relevant Papers:

Xiping Fu, Brendan McCane, Steven Mills, and Michael Albert, 'NOKMeans: Non-orthogonal K-means hashing', in Asian Conference on Computer Vision (ACCV14). pp 162--177.
Xiping Fu, Brendan McCane, Steven Mills, Michael Albert, and Lech Szymanski, 'Auto-JacoBin: Auto-encoder Jacobian Binary Hashing', submitted to PAMI.



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