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Scale Saliency: Applications in Visual Matching, Tracking and
View-Based Object Recognition
Jonathon S. Hare and Paul H. Lewis
Intelligence, Agents, Multimedia GroupDepartment of Electronics and Computer Science
University of Southampton{jsh02r, phl}@ecs.soton.ac.uk
Topics of Discussion
• Introduction
• An overview of the scale-saliency algorithm
• Image Matching and View-Based Recognition
• Tracking
• Future Work
• Salient points or ‘interest-points’ have often been used for various vision tasks
• We have modified and extended an existing saliency algorithm and investigated its application to the tasks of matching, recognition and tracking
Introduction
Scale-Saliency
• Algorithm developed by Kadir & Brady:
• Based on previous work by Gilles
• Defines saliency in terms of local signal complexity or unpredictability, weighted by local self-similarity
• Keypoints are selected from peaks in entropy scale-space, thus the algorithm picks regions in (x, y, scale)
Scale-SaliencyLena R^3 Space
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400 500
x location 0 50
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y location
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scale
Salient regions in the Lena picture The corresponding ℜ3 space
Scale-Saliency
• The original Thesis showed that the algorithm could be used for efficient tracking and matching using intensity pixel values as a descriptor
• We built on this using higher-order descriptors
• Histograms (intensity, RGB, HSI, etc)
• Possibilities for other feature invariants
Matching and View-Based Recognition
• Simple matching algorithm:
• Feature vectors compared using euclidean distance
• Distance(s) of closest features summed for each image to get overall distance
• Image with lowest overall distance is selected as the winning match
Matching and View-Based Recognition
Matching and View-Based Recognition
Matching and View-Based Recognition
Matching Example
• The tracking problem can be posed as a sub-image matching problem
• i.e. we want to track single salient regions across multiple frames
• This can be accomplished using the matching algorithm across consecutive frames
Tracking
• Tracking Examples
• Advantage of this method is that it directly captures changes in scale
Tracking
Tracking
• We have demonstrated a number of practical uses for the scale-saliency algorithm
• Using the scale-saliency algorithm we show how sparse representations of the image can be created and represented by features that are invariant to translation, rotation and scale change
Conclusions
• We have shown that this sparse representation can be used for image matching tasks
• The matching approach can be extended to enable view-based recognition and Content-Based Retrieval
• It is also possible to use the matching technique for tracking by applying it across consecutive video frames
Conclusions
• Future Work
• We are currently investigating how this work can be extended into a facility for recognition of art objects
• We are also researching methods for ensuring spatial consistency of salient points, without the use of graph-matching
Conclusions
Any Questions?
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