System and method for extremely efficient image and pattern recognition and artificial intelligence platform
Specification covers new algorithms, methods, and systems for: Artificial Intelligence; the first application of General-AI (versus Specific, Vertical, or Narrow-AI) (as humans can do) (which also includes Explainable-AI or XAI); addition of reasoning, inference, and cognitive layers/engines to learning module/engine/layer; soft computing; Information Principle; Stratification; Incremental Enlargement Principle; deep-level/detailed recognition, e.g., image recognition (e.g., for action, gesture, emotion, expression, biometrics, fingerprint, tilted or partial-face, OCR, relationship, position, pattern, and object); Big Data analytics; machine learning; crowd-sourcing; classification; clustering; SVM; similarity measures; Enhanced Boltzmann Machines; Enhanced Convolutional Neural Networks; optimization; search engine; ranking; semantic web; context analysis; question-answering system; soft, fuzzy, or un-sharp boundaries/impreciseness/ambiguities/fuzziness in class or set, e.g., for language analysis; Natural Language Processing (NLP); Computing-with-Words (CWW); parsing; machine translation; music, sound, speech, or speaker recognition; video search and analysis (e.g. “intelligent tracking”, with detailed recognition); image annotation; image or color correction; data reliability; Z-Number; Z-Web; Z-Factor; rules engine; playing games; control system; autonomous vehicles or drones; self-diagnosis and self-repair robots; system diagnosis; medical diagnosis/images; genetics; drug discovery; biomedicine; data mining; event prediction; financial forecasting (e.g., for stocks); economics; risk assessment; fraud detection (e.g., for cryptocurrency); e-mail management; database management; indexing and join operation; memory management; data compression; event-centric social network; social behavior; drone/satellite vision/navigation; smart city/home/appliances/IoT; and Image Ad and Referral Networks, for e-commerce, e.g., 3D shoe recognition, from any view angle.
1. A method for image recognition in an image or video recognition platform, with explainability, said method comprising:
an interface receiving an image or video;
wherein said image or video recognition platform comprises a cognition layer;
said interface sending said image or video to said cognition layer;
said interface receiving a first hybrid data;
wherein said first hybrid data comprises non-image data;
wherein said non-image data comprises one or more of the following: voice, music, sound piece, text, number, table, diagram, or graph;
said interface sending said non-image data to said cognition layer;
said cognition layer communicating with a knowledge base repository;
said cognition layer communicating with an experience database;
said cognition layer communicating with a rules engine;
said cognition layer analyzing said image or video and said non-image data simultaneously;
said cognition layer obtaining first properties and parameters from said image or video and said non-image data;
said cognition layer explaining portions of said image or video with said first properties and parameters;
said cognition layer sending said explanation of said portions of said image or video to a first analyzer;
said first analyzer communicating with an object database;
said first analyzer comparing said explanation of said portions of said image or video against said object database;
said first analyzer recognizing each of said portions of said image or video, as a set of object names or identifiers;
said first analyzer sending said set of object names or identifiers from said recognized each of said portions of said image or video, to said cognition layer; and
said cognition layer sending said set of object names or identifiers to an output device.
2. A method for image recognition in an image or video recognition platform, with explainability, said method comprising:
an interface receiving an image or video;
wherein said image or video recognition platform comprises a cognition layer;
said interface sending said image or video to said cognition layer;
said interface receiving a first hybrid data;
wherein said first hybrid data comprises non-image data;
wherein said non-image data comprises one or more of the following: voice, music, sound piece, text, number, table, diagram, or graph;
said interface sending said non-image data to said cognition layer;
said cognition layer communicating with a knowledge base repository;
said cognition layer communicating with a contradiction analysis subsystem;
said cognition layer analyzing said image or video and said non-image data simultaneously;
said cognition layer obtaining first properties and parameters from said image or video and said non-image data;
said cognition layer explaining portions of said image or video with said first properties and parameters;
said cognition layer sending said explanation of said portions of said image or video to a first analyzer;
said first analyzer communicating with an object database;
said first analyzer comparing said explanation of said portions of said image or video against said object database;
said first analyzer recognizing each of said portions of said image or video, as a set of object names or identifiers;
said first analyzer sending said set of object names or identifiers from said recognized each of said portions of said image or video, to said cognition layer; and
said cognition layer sending said set of object names or identifiers to an output device.
3. A method for image recognition in an image or video recognition platform, with explainability, said method comprising:
an interface receiving an image or video;
wherein said image or video recognition platform comprises a cognition layer;
said interface sending said image or video to said cognition layer;
said interface receiving a first hybrid data;
wherein said first hybrid data comprises non-image data;
wherein said non-image data comprises one or more of the following: voice, music, sound piece, text, number, table, diagram, or graph;
said interface sending said non-image data to said cognition layer;
said cognition layer communicating with a logic subsystem;
said cognition layer communicating with an inference subsystem;
said cognition layer analyzing said image or video and said non-image data simultaneously;
said cognition layer obtaining first properties and parameters from said image or video and said non-image data;
said cognition layer explaining portions of said image or video with said first properties and parameters;
said cognition layer sending said explanation of said portions of said image or video to a first analyzer;
said first analyzer communicating with an object database;
said first analyzer comparing said explanation of said portions of said image or video against said object database;
said first analyzer recognizing each of said portions of said image or video, as a set of object names or identifiers;
said first analyzer sending said set of object names or identifiers from said recognized each of said portions of said image or video, to said cognition layer; and
said cognition layer sending said set of object names or identifiers to an output device.
4. The method for image recognition in an image or video recognition platform, with explainability, as recited in claim 1 , wherein said image or video is a still image.
5. The method for image recognition in an image or video recognition platform, with explainability, as recited in claim 1 , wherein said image or video is a frame of a video.
6. The method for image recognition in an image or video recognition platform, with explainability, as recited in claim 1 , wherein said image or video is a section of a frame of a video.
7. The method for image recognition in an image or video recognition platform, with explainability, as recited in claim 1 , wherein said image or video recognition platform is connected to an intelligent object-tracking system.
8. The method for image recognition in an image or video recognition platform, with explainability, as recited in claim 1 , wherein said image or video recognition platform is connected to an intelligent human-tracking system.
9. The method for image recognition in an image or video recognition platform, with explainability, as recited in claim 1 , wherein said image or video recognition platform is on a video camera.
10. The method for image recognition in an image or video recognition platform, with explainability, as recited in claim 1 , wherein said image or video recognition platform is on an autonomous or semi-autonomous vehicle.
11. The method for image recognition in an image or video recognition platform, with explainability, as recited in claim 1 , wherein said image or video recognition platform is on a drone, airplane, satellite, boat, or submarine.
12. The method for image recognition in an image or video recognition platform, with explainability, as recited in claim 1 , wherein said image or video recognition platform is connected to an airport camera, a security camera, or a smart city camera.
13. The method for image recognition in an image or video recognition platform, with explainability, as recited in claim 1 , wherein said image or video is a face image.
14. The method for image recognition in an image or video recognition platform, with explainability, as recited in claim 1 , wherein said image or video is a biometrics image.
15. The method for image recognition in an image or video recognition platform, with explainability, as recited in claim 1 , wherein said image or video is a medical image.
16. The method for image recognition in an image or video recognition platform, with explainability, as recited in claim 1 , wherein said image or video recognition platform is connected to a smart home or a smart appliance.
17. The method for image recognition in an image or video recognition platform, with explainability, as recited in claim 1 , wherein said image or video recognition platform is connected to an e-commerce recommendation engine.
18. The method for image recognition in an image or video recognition platform, with explainability, as recited in claim 1 , wherein said image or video recognition platform is connected to an e-commerce store or an e-commerce search engine.
19. The method for image recognition in an image or video recognition platform, with explainability, as recited in claim 1 , wherein said image or video recognition platform is a part of a navigation system of a vehicle or drone.
20. The method for image recognition in an image or video recognition platform, with explainability, as recited in claim 1 , wherein said image or video recognition platform is connected to a GPS or coordinate analysis system.
21. The method for image recognition in an image or video recognition platform, with explainability, as recited in claim 1 , wherein said image or video recognition platform is a part of a multi-camera system, multi-sensors, multi-data detectors, or multi-spectrum detectors.
22. The method for image recognition in an image or video recognition platform, with explainability, as recited in claim 1 , said method comprises: communicating with an inference engine.
23. The method for image recognition in an image or video recognition platform, with explainability, as recited in claim 1 , said method comprises: communicating with a logic engine.
24. The method for image recognition in an image or video recognition platform, with explainability, as recited in claim 1 , said method comprises: communicating with an outside knowledge base or data feed.
25. The method for image recognition in an image or video recognition platform, with explainability, as recited in claim 1 , wherein said image or video recognition platform is a part of a cumulative, scalable, expandable, reusable, modular, and continuous machine learning platform.
26. The method for image recognition in an image or video recognition platform, with explainability, as recited in claim 1 , said method comprises: communicating with a cross-domain learning subsystem.
27. The method for image recognition in an image or video recognition platform, with explainability, as recited in claim 1 , said method comprises: detecting an unexpected or rare situation, parameter, or feature.
28. The method for image recognition in an image or video recognition platform, with explainability, as recited in claim 1 , wherein said image or video recognition platform is a part of a generalized learning system with concept-learning, cross-domain-learning, concept-abstraction, and concept-generalization.
29. The method for image recognition in an image or video recognition platform, with explainability, as recited in claim 1 , wherein said image or video recognition platform is a part of a level-5 self-driving vehicle system.
30. The method for image recognition in an image or video recognition platform, with explainability, as recited in claim 1 , wherein said image or video recognition platform is a part of a detailed 3D object recognition system for situational awareness or environment sensing.
31. The method for image recognition in an image or video recognition platform, with explainability, as recited in claim 1 , wherein said image or video recognition platform is connected to infrared and visible light detectors, sensors, or cameras.