Classifier Pipeline¶

Structuring Ideas¶

• Data-flow layer + control layer
  • E.g. central controller manages component states according to some protocol/rules

• Alternative: Subgraph lifecycles
  • E.g. learning subgraph between data buffer and learner)

Requirements¶

Training¶

• Sequential processing: data source, keypoint, descriptor, feature
• Processing Strategies (in CCA speech)
  • First possibility
    • As fast as possible
    • Flow control is necessary
    • Usually, all inputs are processed
  • Second possibility
    • "Live" data source (i.e. camera)
• Multiple phases
  • Collection
  • Actual training
  • Model is transmitted from learner to classifier

Classification¶

• Static, linear data flow: source, keypoint, descriptor, feature, classifier
• No buffering/persistence required

Non-functional Requirements¶

• Parallel / distributed processing
• Visualization (when and what)
• Model description / specification (do skeletons exist?)

Related work¶

• gstreamer
  • usability is an issue
• IceWing
  • data flow specification
  • parallel processing (?)
  • distributed processing (?)
  • support for non-image data is not existent
  • usability (?)
  • no support for collection, training, classification phases / states