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glam/schemas/20251121/linkml/modules/classes/VideoAnnotation.yaml

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id: https://nde.nl/ontology/hc/class/VideoAnnotation
name: video_annotation_class
title: Video Annotation Class
imports:
- linkml:types
- ./VideoTextContent
- ./VideoTimeSegment
- ./AnnotationMotivationType
- ./AnnotationMotivationTypes
- ../slots/filters_or_filtered
- ./DetectedEntity
- ./DetectionThreshold
- ../slots/has_or_had_treshold
- ./VideoFrame
- ../slots/has_or_had_quantity
- ../slots/has_or_had_unit
- ./Quantity
- ./Unit
- ../slots/includes_bounding_box
- ../slots/includes_segmentation_mask
- ../slots/keyframe_extraction
- ../slots/model_architecture
- ../slots/model_task
- ../slots/specificity_annotation
- ../slots/has_or_had_score
- ../slots/analyzes_or_analyzed
- ./SpecificityAnnotation
- ./TemplateSpecificityScore
- ./TemplateSpecificityType
- ./TemplateSpecificityTypes
- ../enums/AnnotationTypeEnum
prefixes:
linkml: https://w3id.org/linkml/
hc: https://nde.nl/ontology/hc/
schema: http://schema.org/
dcterms: http://purl.org/dc/terms/
prov: http://www.w3.org/ns/prov#
crm: http://www.cidoc-crm.org/cidoc-crm/
oa: http://www.w3.org/ns/oa#
as: https://www.w3.org/ns/activitystreams#
default_prefix: hc
classes:
VideoAnnotation:
is_a: VideoTextContent
class_uri: oa:Annotation
abstract: true
description: "Abstract base class for computer vision and multimodal video annotations.\n\n**DEFINITION**:\n\nVideoAnnotation represents structured information derived from visual\nanalysis of video content. This includes:\n\n| Subclass | Analysis Type | Output |\n|----------|---------------|--------|\n| VideoSceneAnnotation | Shot/scene detection | Scene boundaries, types |\n| VideoObjectAnnotation | Object detection | Objects, faces, logos |\n| VideoOCRAnnotation | Text extraction | On-screen text (OCR) |\n\n**RELATIONSHIP TO W3C WEB ANNOTATION**:\n\nVideoAnnotation aligns with the W3C Web Annotation Data Model:\n\n```turtle\n:annotation a oa:Annotation ;\n oa:hasBody :detection_result ;\n oa:hasTarget [\n oa:hasSource :video ;\n oa:hasSelector [\n a oa:FragmentSelector ;\n dcterms:conformsTo <http://www.w3.org/TR/media-frags/> ;\n rdf:value \"t=30,35\"\n ]\n ] ;\n oa:motivatedBy oa:classifying .\n```\n\n**FRAME-BASED\
\ ANALYSIS**:\n\nUnlike audio transcription (continuous stream), video annotation is\ntypically frame-based:\n\n- `frame_sample_rate`: Frames analyzed per second (e.g., 1 fps, 5 fps)\n- `analyzes_or_analyzed`: Total frames processed\n- Higher sample rates = more detections but higher compute cost\n\n**DETECTION THRESHOLDS**:\n\nCV models output confidence scores. Thresholds filter noise:\n\n| Threshold | Use Case |\n|-----------|----------|\n| 0.9+ | High precision, production display |\n| 0.7-0.9 | Balanced, general use |\n| 0.5-0.7 | High recall, research/review |\n| < 0.5 | Raw output, needs filtering |\n\n**MODEL ARCHITECTURE TRACKING**:\n\nDifferent model architectures have different characteristics:\n\n| Architecture | Examples | Strengths |\n|--------------|----------|-----------|\n| CNN | ResNet, VGG | Fast inference, good for objects |\n| Transformer | ViT, CLIP | Better context, multimodal |\n| Hybrid | DETR, Swin | Balance of speed and accuracy |\n\n**HERITAGE INSTITUTION\
\ CONTEXT**:\n\nVideo annotations enable:\n- **Discovery**: Find videos containing specific objects/artworks\n- **Accessibility**: Scene descriptions for visually impaired\n- **Research**: Analyze visual content at scale\n- **Preservation**: Document visual content as text\n- **Linking**: Connect detected artworks to collection records\n\n**CIDOC-CRM E13_Attribute_Assignment**:\n\nAnnotations are attribute assignments - asserting properties about\nvideo segments. The CV model or human annotator is the assigning agent.\n"
exact_mappings:
- oa:Annotation
close_mappings:
- crm:E13_Attribute_Assignment
related_mappings:
- as:Activity
- schema:ClaimReview
slots:
- has_or_had_rationale
- contains_or_contained
- has_or_had_type
- filters_or_filtered
- includes_bounding_box
- includes_segmentation_mask
- keyframe_extraction
- model_architecture
- model_task
- specificity_annotation
- has_or_had_score
- analyzes_or_analyzed
slot_usage:
has_or_had_type:
range: AnnotationType
required: true
description: Type of annotation (Object detection, Scene detection, etc.)
examples:
- value:
has_or_had_code: OBJECT_DETECTION
has_or_had_label: Object Detection
description: Object and face detection annotation
contains_or_contained:
range: Segment
multivalued: true
required: false
inlined_as_list: true
description: Segments (temporal or spatial) identified by the annotation. MIGRATED from has_annotation_segment per Rule 53.
examples:
- value:
has_or_had_label: Night Watch painting visible
has_or_had_description: 30.0 - 35.0 seconds
description: Object detection segment
has_or_had_rationale:
range: Rationale
required: false
description: Motivation for the annotation.
examples:
- value:
has_or_had_label: ClassifyingMotivation
description: Annotation for classification purposes
filters_or_filtered:
description: "MIGRATED 2026-01-25: Replaces detection_count and detection_threshold slots.\n\nLinks to DetectedEntity which contains:\n- has_or_had_quantity \u2192 Quantity (for detection_count)\n- has_or_had_treshold \u2192 DetectionThreshold (for detection_threshold)\n\n**Migration Pattern**:\n- Old: detection_count: 342, detection_threshold: 0.5\n- New: filters_or_filtered \u2192 DetectedEntity with structured data\n"
range: DetectedEntity
inlined: true
required: false
examples:
- value:
has_or_had_quantity:
quantity_value: 342
has_or_had_unit:
unit_value: detections
has_or_had_treshold:
threshold_value: 0.5
threshold_type: MINIMUM
description: 342 detections at 0.5 confidence threshold
- value:
has_or_had_quantity:
quantity_value: 89
has_or_had_unit:
unit_value: detections
has_or_had_treshold:
threshold_value: 0.9
threshold_type: MINIMUM
has_or_had_label: High Precision
description: 89 high-confidence detections
analyzes_or_analyzed:
description: "MIGRATED 2026-01-22: Now supports VideoFrame class for frame_sample_rate migration.\n\nFrame analysis information including:\n- Total frames analyzed (integer, legacy pattern)\n- Frame sample rate and analysis parameters (VideoFrame class)\n\nMIGRATED SLOTS:\n- frame_sample_rate \u2192 VideoFrame.has_or_had_quantity with unit \"samples per second\"\n"
range: VideoFrame
inlined: true
required: false
examples:
- value:
has_or_had_quantity:
quantity_value: 1.0
quantity_type: FRAME_SAMPLE_RATE
has_or_had_unit:
unit_value: samples per second
frame_count: 1800
description: Analyzed 1,800 frames at 1 fps (30 min video)
- value:
has_or_had_quantity:
quantity_value: 5.0
quantity_type: FRAME_SAMPLE_RATE
has_or_had_unit:
unit_value: fps
description: 5 frames per second sample rate
keyframe_extraction:
range: boolean
required: false
examples:
- value: true
description: Used keyframe extraction
model_architecture:
range: string
required: false
examples:
- value: Transformer
description: Vision Transformer architecture
- value: CNN
description: Convolutional Neural Network
model_task:
range: string
required: false
examples:
- value: detection
description: Object detection task
- value: captioning
description: Video captioning task
includes_bounding_box:
range: boolean
required: false
examples:
- value: true
description: Includes bounding box coordinates
includes_segmentation_mask:
range: boolean
required: false
examples:
- value: false
description: No segmentation masks included
comments:
- Abstract base for all CV/multimodal video annotations
- Extends VideoTextContent with frame-based analysis parameters
- W3C Web Annotation compatible structure
- Supports both temporal and spatial annotation
- Tracks detection thresholds and model architecture
see_also:
- https://www.w3.org/TR/annotation-model/
- http://www.cidoc-crm.org/cidoc-crm/E13_Attribute_Assignment
- https://iiif.io/api/presentation/3.0/
annotations:
specificity_score: 0.1
specificity_rationale: Generic utility class/slot created during migration
custodian_types: "['*']"
custodian_types_rationale: Universal utility concept
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