A Performance Characterization Algorithm for Symbol Localization Mathieu Delalandre 1, Jean-Yves Ramel 2, Ernest Valveny 1 and Muhammad Muzzamil Luqman.

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Presentation transcript:

A Performance Characterization Algorithm for Symbol Localization Mathieu Delalandre 1, Jean-Yves Ramel 2, Ernest Valveny 1 and Muhammad Muzzamil Luqman 2 1 CVC, Barcelona city, Spain 2 LI Laboratory, Tours city, France GREC 2009 Workshop La Rochelle, France Thursday 23th of July 2009

A Performance Characterization Algorithm for Symbol Localization Recognition Spotting r1 r2 r3 sof a ski n tub doo r docume nt databas e learnin g databa se Query By Example (QBE) rank labels Symbol localization systems (recognition and spotting) Spotting/Recognition System Groundtruth Matching localization results with groundtruth Region Of Interest Characterization measures sofa skin tub door Labels r1 r2 r3 Ranks QBE truth results Learning Performance characterization To make the correspondence in term of localization To compute characterization measures (recall, precision, recognition rates, etc.)

A Performance Characterization Algorithm for Symbol Localization Layout analysis [Antonacopoulos1999] Text/graphics separation [Liu1997] truth results Single : an object in groundtruth matches only with one detected object. Split : two objects in groundtruth match with one detected object. Merge : an object in groundtruth matches with two detected objects. False alarm : a detected object doesn't match with any object in groundtruth. Miss : an object in groundtruth doesn't match with any detected object. Matching localization results with groundtruth Symbol spotting [Rusinol2009] char and text boxes groundtr uth results groundtr uth results isothetic polygons groundtr uth results Convex hulls

A Performance Characterization Algorithm for Symbol Localization probability error detection rate p1p1 p3p3 p2p2 Groundtruth, gravity centers, contours Result points Highest probabilities Lowest probabilities Localization comparison Probability scores Matching algorithm GroundtruthResults probability error detection rate

A Performance Characterization Algorithm for Symbol Localization Localization comparison Probability scores Matching algorithm GroundtruthResults probability error detection rate Result point Intersection line Intersection point Groundtruth, gravity center, contours l gi l gr L g ri c probability error detection rate p1p1 p3p3 p2p2 Groundtruth, gravity centers, contours Result points Highest probabilities Lowest probabilities

A Performance Characterization Algorithm for Symbol Localization Localization comparison Probability scores Matching algorithm GroundtruthResults probability error detection rate s2s2 s1s1 s3s3 g1g1 r g2g2 g3g3 g2g2 s2s2 s3s3 r g1g1 g3g3 s Groundtruth points Result point r gigi s2s2 s1s1 s3s3 g1g1 r g2g2 g3g3 null probabilities, = equidistant case highest probabilities, = nearest points maximum probability, = equality case probability error detection rate p1p1 p3p3 p2p2 Groundtruth, gravity centers, contours Result points Highest probabilities Lowest probabilities

A Performance Characterization Algorithm for Symbol Localization Groundtruth points Result points r1r1 r2r2 rqrq g1g1 g2g2 gngn … … d g1 =2 d gn =0 d r1 =1 detection rates score error ε single (T s ) alarm (T f ) multiple (T m ) Localization comparison Probability scores Matching algorithm GroundtruthResults probability error detection rate probability error detection rate p1p1 p3p3 p2p2 Groundtruth, gravity centers, contours Result points Highest probabilities Lowest probabilities

Qureshi’2008 A Performance Characterization Algorithm for Symbol Localization Drawing levelSymbol level Settingbackgrounds5models16 Datasetimages100symbols2521 Settingbackgrounds5models17 Datasetimages100symbols1340 floorplans diagrams false alarm (T f ) detection rates score error 1 single (T s ) multiple (T m ) max score error 1 detection rates false alarm (T f ) single (T s ) multiple (T m ) max floorplans diagrams Sco re 0,11 Ts0,57 Tf0,31 Tm0,20 Sco re 0,20 Ts0,62 Tf0,06 Tm0,37 max

Conclusion and perspectives Conclusion A new fuzzy way to evaluate object localization distribution of matching cases regarding a “confidence rate” Experimentation with a real system electrical and architectural drawings, 200 test images, 3821 symbols Perspectives Comparison of results (comparison of curves) Extending experiments several systems, to add noise, scalability, real datasets