Recognition Using Visual Phrases

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Recognition Using Visual Phrases. CVPR 2011 Best Student Paper. Outline. Introduction Related Works Approach Phrasal Recognition Decoding Multiple Detections Results Discussion. Introduction. Introduction. Visual Phrases Traditional approach Detect objects (person , dog, horse …)
Recognition Using Visual Phrases CVPR 2011 Best Student Paper Outline
  • Introduction
  • Related Works
  • Approach
  • Phrasal Recognition
  • Decoding Multiple Detections
  • Results
  • Discussion
  • Introduction Introduction
  • Visual Phrases
  • Traditional approach
  • Detect objects (person, dog, horse…)
  • Relation between objects
  • NMS(non-maximum suppression)
  • other
  • Disadvantage
  • Introduction Introduction
  • Contributions
  • Introducing visual phrases as categories for recognition
  • Introducing a novel dataset for phrasal recognition
  • The state of the art methods of modeling interactions
  • A decoding algorithm
  • Performance results in multi-class object recognition
  • Related Work
  • Object Recognition
  • Object Recognition
  • Deformable templates [IEEE2001,CVPR1998]
  • Part base model [CVPR2005,CVPR2003]
  • Detectors
  • Deformable based model [IEEE2010]
  • Related Work
  • Object Interactions
  • Focus on relation [ECCV2008]
  • Person with object [CVPR 2010]
  • Objects [ECCV2010]
  • Relation of objects [ICCV2010]
  • left, right, top, down
  • label
  • weight, confidence
  • Related Work
  • Scene understanding
  • Represent scenes as with global features that take into account general information about images [Vision2001,CVPR2006]
  • Cluster[ECCV2008]
  • Related Work
  • Machine translation
  • Statistical translation methods[Press2010]
  • Translation model
  • Language model
  • A decoding algorithm
  • Output: aquery sentence
  • Allow multiple to multiple translation
  • Phrasal Recognition
  • Phrasal Recognition Dataset
  • select 8 obj. class (Pascal VOC 2008)
  • person, bike, car, dog, horse, bottle, sofa, chair
  • A list of 17 visual phrases + background class
  • Dog jumping ,horse jumping, person riding horse…
  • Phrasal Recognition Phrasal Recognition
  • Datasets
  • 2769 images (822 negative image)
  • 120 examples, average of each classes
  • 5067 bounding boxes(1796 phrases,3271 objects)
  • The complexity of Visual Phrases crease
  • The number of training example decrease
  • Phrasal Recognition
  • Appearance models
  • Deformation part model
  • 17 phrases in our dataset using provided bounding boxes
  • 8 categories from Pascal are used as models for objects
  • Decoding Multiple Detections
  • NMS decoding
  • Perfect detectors with excellent tightly tuned models
  • Natural decoding strategy better than NMS on interaction
  • Greedily search the space of labels
  • Well designed feature (nearby)
  • All detector responses Final outcome Decoding Decoding Multiple Detections
  • Decoding process
  • We compare our decoding algorithm with that of [2] on our phrase dataset
  • Step1: construct the feature
  • Step2: running algorithm to learn a set of weights that rescore the confidences of the bounding boxes based on interactions
  • Step3: We again rescore until optimal
  • Discriminative models for multi-class object layout Decoding Multiple Detections
  • : a bounding box in an image
  • An image is represented as a collection of overlapping Bounding boxes
  • X = { : i=1….M},M is the total num of bounding box
  • K is different categories
  • 1 ,
  • 1
  • 1 is the score of image X with Y
  • is the set of weights that corresponds to
  • the class of the bounding box
  • Decoding Multiple Detections
  • Representation
  • Image = bounding boxes
  • Confidence
  • Overlap
  • Size ratio
  • Relation
  • Above, Below, overlapping
  • Window, category, spatial bins
  • Representation has K*3*3+1 dimensions
  • Decoding Multiple Detections
  • Inference
  • assume bounding boxes are independent given their features
  • 1
  • Decoding Multiple Detections
  • Learning
  • A form of max margin structure learning
  • 1
  • Decoding Multiple Detections
  • 1
  • our inner maximization is exact and very fast. We solve this optimization problem by subgradient descent method as follows.
  • Result
  • Single category detection
  • deformable part models for 17 visual phrase
  • the trained models from for objects
  • Use PASCAL dataset : 50 positive and 150 negative examples
  • Show Precision-Recall (PR) curves
  • Trained these detectors with at most 50 positive examples
  • Result Result Result Result Result
  • Decoding
  • [2] C. F. C. Desai, D. Ramanan. Discriminative models for multi-class object layout. In ICCV, 2010. Result Result Discussion
  • Introduce visual phrases, phrasal recognition dataset
  • A coding algorithm
  • The dimensionality of our features grows with the number of categories
  • Future Work
  • the relations between attributes and objects
  • parts and objects
  • visual phrases and scenes
  • objects and visual phrases mirror one another
  • Discussion
  • Experience
  • Low complexity
  • Use less data to detection
  • Features grows with the number of categories (exponential 2n)
  • But we don’t need to consider all of the categories when we model the interactions
  • Building long enough phrase tables is still a challenge
  • Related Search
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