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IPC Subgroup
G06V 10/4

Extraction of image or video features

Introduced: January 2022

Full Title

Arrangements for image or video recognition or understanding > Extraction of image or video features

Classification Context

Section:
PHYSICS
Class:
COMPUTING OR CALCULATING; COUNTING
Subclass:
IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING

Additional Content

Glossary

BoW bag of words, a model originally developed for natural language processing; when applied to images, it represents an image by a histogram of visual words, each visual word representing a specific part of the feature space. edge edges region in the image, at which the image exhibits a strong luminance gradient. GLCM grey-level co-occurrence matrix HOG histogram of oriented gradients, a feature descriptor described by N Dalal and B Triggs SIFT scale-invariant feature transform, a feature detection algorithm SURF speeded up robust features, a feature descriptor

Application references

Recognition of scene and scene-specific elements Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition Image or video recognition or understanding of human-related, animal-related or biometric patterns in image or video data Recognition of fingerprints or palmprints Recognition of vascular patterns Recognition of human faces, e.g. facial parts, sketches or expressions within images or video data Recognition of eye characteristics within image or video data, e.g. of the iris

11 direct subcodes

Child Classifications

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  • G06V 10/44 Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
  • G06V 10/46 Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features
  • G06V 10/48 by mapping characteristic values of the pattern into a parameter space, e.g. Hough transformation
  • G06V 10/5 by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis
  • G06V 10/52 Scale-space analysis, e.g. wavelet analysis
  • G06V 10/54 relating to texture
  • G06V 10/56 relating to colour
  • G06V 10/58 relating to hyperspectral data
  • G06V 10/6 relating to illumination properties, e.g. using a reflectance or lighting model
  • G06V 10/62 relating to a temporal dimension, e.g. time-based feature extraction; Pattern tracking

Top Applicants

Top 10 applicants by patent filingsfor class G06, 2013–2023, worldwide · Source: EPO PATSTAT

  1. SAMSUNG ELECTRONICS COMPANY KR 66,669
  2. IBM (INTERNATIONAL BUSINESS MACHINES CORPORATION) US 62,313
  3. MICROSOFT TECHNOLOGY LICENSING US 41,918
  4. GOOGLE US 32,969
  5. SGCC(STATE GRID CORPORATION OF CHINA) 30,822
  6. INTEL CORPORATION US 30,010
  7. TENCENT TECHNOLOGY (SHENZHEN) COMPANY 28,235
  8. HUAWEI TECHNOLOGIES COMPANY CN 26,079
  9. APPLE US 21,891
  10. HUAWEI TECHNOLOGIES COMPANY 20,505