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RESEARCH PRODUCT
Rethinking the sGLOH Descriptor
Fabio BellaviaCarlo Colombosubject
Cascade matching0209 industrial biotechnologyHistogram binarizationRFDComputer scienceGLOHComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION02 engineering and technologyCNN descriptorLIOP020901 industrial engineering & automationMROGHArtificial IntelligenceRobustness (computer science)Keypoint matchingSIFTHistogram0202 electrical engineering electronic engineering information engineeringSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniSettore INF/01 - Informaticabusiness.industryApplied MathematicsCognitive neuroscience of visual object recognitionPattern recognitionRotation invariant descriptorsGLOHMIOPComputational Theory and MathematicsKeypoint matching SIFT sGLOH RFDs LIOP MIOP MROGH CNN descriptors rotation invariant descriptors histogram binarization cascade matchingPrincipal component analysis020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionArtificial intelligencebusinessSoftwaredescription
sGLOH (shifting GLOH) is a histogram-based keypoint descriptor that can be associated to multiple quantized rotations of the keypoint patch without any recomputation. This property can be exploited to define the best distance between two descriptor vectors, thus avoiding computing the dominant orientation. In addition, sGLOH can reject incongruous correspondences by adding a global constraint on the rotations either as an a priori knowledge or based on the data. This paper thoroughly reconsiders sGLOH and improves it in terms of robustness, speed and descriptor dimension. The revised sGLOH embeds more quantized rotations, thus yielding more correct matches. A novel fast matching scheme is also designed, which significantly reduces both computation time and memory usage. In addition, a new binarization technique based on comparisons inside each descriptor histogram is defined, yielding a more compact, faster, yet robust alternative. Results on an exhaustive comparative experimental evaluation show that the revised sGLOH descriptor incorporating the above ideas and combining them according to task requirements, improves in most cases the state of the art in both image matching and object recognition.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2018-04-01 |