TAILIEUCHUNG - Generative learning methods for bags of features

Generative methods, topic discovery in images, application of pLSA action recognition, multiple actions,. As the main contents of the lecture "Generative learning methods for bags of features". Each of your content and references for additional lectures will serve the needs of learning and research. | Generative learning methods for bags of features Model the probability of a bag of features given a class Many slides adapted from Fei-Fei Li, Rob Fergus, and Antonio Torralba Generative methods We will cover two models, both inspired by text document analysis: Naïve Bayes Probabilistic Latent Semantic Analysis The Naïve Bayes model Csurka et al. 2004 Assume that each feature is conditionally independent given the class fi: ith feature in the image N: number of features in the image The Naïve Bayes model Csurka et al. 2004 Assume that each feature is conditionally independent given the class wj: jth visual word in the vocabulary M: size of visual vocabulary n(wj): number of features of type wj in the image fi: ith feature in the image N: number of features in the image The Naïve Bayes model Csurka et al. 2004 Assume that each feature is conditionally independent given the class No. of features of type wj in training images of class c Total no. of features in training images of class c p(wj

• Đồ họa - Thiết kế - Flash
• Computer Vision
• Imagine processing
• Computer science
• Generative learning methods
• Generative methods
• Action recognition
• Computer vision algorithms
• Computer vision applications
• Image formation
• Feature detection
• Journal of technology and science education
• Experiences using an open source software library
• Teach computer vision subjects
• Computer vision subjects
• Computer vision teaching
• Computer vision difficult
• Aerial perspective
• Local ambiguity
• Introduction to computer vision
• Brief history
• Nearby fields
• Related disciplines
• International Journal of Computer Networks and Communications Security
• Application intelligence and vision in internet of things
• Vision in internet of things
• Wireless sensor and actuators
• Radio Frequency Identification
• Practical Computer
• Vision
• with SimpleCV
• Nathan Oostendorp
• Anthony Oliver
• and Katherine Scott
• Mastering OpenCV
• Vision Projects
• Daniel Lélis Baggio
• Khvedchenia Ievgen
• Scripting Cookbook
• Themes Design
• Mapping with Drupal
• OpenCV 2
• Computer Vision Application
• Programming Cookbook
• Generating Image Descriptions
• Computer Vision Detections
• Margaret Mitchell
• scientific reports
• model language
• process natural language
• Nuclear energy
• Nuclear power plant outage
• Automatically detecting abnormal human
• Field operation and preparation
• Data driven simulation
• The pinhole projection model
• Cameras with lenses
• Digital cameras
• Lesson Capturing light
• Capturing light
• Radiometry of thin lenses
• The physics of light
• Cone sensitivity
• Uniform color spaces
• Lesson Linear filtering
• Linear filtering
• Key properties
• Edge detection
• Origin of edges
• characterizing edges
• Feature extraction
• Corners and blobs
• Finding corners
• Vertical least squares
• Total least squares
• General curves
• The hough transform
• Voting schemes
• Hough transform
• Image alignment
• Bing streetside images
• Single view geometry
• Pinhole camera model
• Principal point
• Two view geometry
• View geometry
• epipolar geometry
• Binocular stereo
• Depth from disparity
• Stereo matching
• Multiple baseline stereo
• Plane sweep stereo
• Volumetric stereo
• Structure from motion
• Projective ambiguity
• Similarity ambiguity
• Object recognition
• History and overview
• Scene categorization