Awesome

Fine-grained Recognition with Part-Transfer

Computer Vision Group, Friedrich Schiller University of Jena

Authors: Christoph Goering, Erik Rodner, Alexander Freytag

contact: Erik.Rodner@uni-jena.de

Needed libraries and third party software

1. vlfeat - for extracting features - http://www.vlfeat.org
2. yael - fast, multithreaded k-means - https://gforge.inria.fr/projects/yael/
3. opencv - only used for grabcut - http://opencv.org
4. liblinear - http://www.csie.ntu.edu.tw/~cjlin/liblinear/
5. color names - http://lear.inrialpes.fr/people/vandeweijer/software

Notes:

• if installed, the parallel toolbox can be used. just uncomment the parfor in vlfeatExtractFeatures and vlfeatCreateCodebook

• mex-wrapper for grabcut needs to be compiled before first use

Usage (standard experiments)

    recRate = experimentParts('cub200_2011',nrClasses, ...
        config, configParts)

• nrClasses = 200 | 14 | 3
• config - parameters to influence extraction of global features, a list can be found in experimentGeneral_extractGlobalFeatures.m
• configParts - for features extracted form parts, list can be found in experimentGeneral_extractPartFeatures.m

Examples

1. use default values:

    recRate = experimentParts('cub200_2011',nrClasses, ...
        struct([]), struct([]))

2. do not use global features:

    recRate = experimentParts('cub200_2011',nrClasses, ...
        struct('useGlobal','no'), struct([]))

3. do not use part features:

    recRate = experimentParts('cub200_2011',nrClasses, ...
        struct([]), struct('useParts','none'))

4. use part features, estimated using nearest neighbour:

    recRate = experimentParts('cub200_2011',nrClasses, ...
        struct([]), struct('useParts','nn'))

5. do not use grabcut segmentation:

    recRate = experimentParts('cub200_2011',nrClasses, ...
        struct('preprocessing_useMask','none'), ...
        struct('useParts','none'))

6. use the k-best part estimations:

    recRate = experimentParts_knn('cub200_2011',nrClasses, ...
        struct([]), struct([]))

Details of the algorithm

The algorithm is described in detail in the corresponding paper, here, we just give a very brief overview and mention some additional aspects:

• opoonentSift and colorname features are used
• classification is done using liblinear and an approximated chi square kernel
• global features can be extracted from the whole image, the provided bounding box, or from a grabcut segmentation
• left and right instances of part features are pooled
• for classification all features are concatenated
• part transfer is based on HOG feature matching