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218 lines
7.3 KiB
ReStructuredText
218 lines
7.3 KiB
ReStructuredText
Object Categorization
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=====================
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.. highlight:: cpp
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Some approaches based on local 2D features and used to object categorization
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are described in this section.
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.. index:: BOWTrainer
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.. _BOWTrainer:
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BOWTrainer
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----------
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.. c:type:: BOWTrainer
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Abstract base class for training ''bag of visual words'' vocabulary from a set of descriptors.
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See e.g. ''Visual Categorization with Bags of Keypoints'' of Gabriella Csurka, Christopher R. Dance,
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Lixin Fan, Jutta Willamowski, Cedric Bray, 2004. ::
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class BOWTrainer
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{
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public:
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BOWTrainer(){}
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virtual ~BOWTrainer(){}
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void add( const Mat& descriptors );
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const vector<Mat>& getDescriptors() const;
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int descripotorsCount() const;
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virtual void clear();
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virtual Mat cluster() const = 0;
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virtual Mat cluster( const Mat& descriptors ) const = 0;
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protected:
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...
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};
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..
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.. index:: BOWTrainer::add
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BOWTrainer::add
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------------------- ````
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.. c:function:: void BOWTrainer::add( const Mat\& descriptors )
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Add descriptors to training set. The training set will be clustered using clustermethod to construct vocabulary.
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:param descriptors: Descriptors to add to training set. Each row of ``descriptors`` matrix is a one descriptor.
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.. index:: BOWTrainer::getDescriptors
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BOWTrainer::getDescriptors
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------------------------------
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.. c:function:: const vector<Mat>\& BOWTrainer::getDescriptors() const
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Returns training set of descriptors.
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.. index:: BOWTrainer::descripotorsCount
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BOWTrainer::descripotorsCount
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---------------------------------
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.. c:function:: const vector<Mat>\& BOWTrainer::descripotorsCount() const
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Returns count of all descriptors stored in the training set.
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.. index:: BOWTrainer::cluster
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BOWTrainer::cluster
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-----------------------
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.. c:function:: Mat BOWTrainer::cluster() const
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Cluster train descriptors. Vocabulary consists from cluster centers. So this method
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returns vocabulary. In first method variant the stored in object train descriptors will be
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clustered, in second variant -- input descriptors will be clustered.
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.. c:function:: Mat BOWTrainer::cluster( const Mat\& descriptors ) const
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:param descriptors: Descriptors to cluster. Each row of ``descriptors`` matrix is a one descriptor. Descriptors will not be added
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to the inner train descriptor set.
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.. index:: BOWKMeansTrainer
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.. _BOWKMeansTrainer:
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BOWKMeansTrainer
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----------------
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.. c:type:: BOWKMeansTrainer
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:func:`kmeans` based class to train visual vocabulary using the ''bag of visual words'' approach. ::
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class BOWKMeansTrainer : public BOWTrainer
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{
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public:
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BOWKMeansTrainer( int clusterCount, const TermCriteria& termcrit=TermCriteria(),
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int attempts=3, int flags=KMEANS_PP_CENTERS );
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virtual ~BOWKMeansTrainer(){}
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// Returns trained vocabulary (i.e. cluster centers).
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virtual Mat cluster() const;
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virtual Mat cluster( const Mat& descriptors ) const;
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protected:
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...
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};
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..
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To gain an understanding of constructor parameters see
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:func:`kmeans` function
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arguments.
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.. index:: BOWImgDescriptorExtractor
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.. _BOWImgDescriptorExtractor:
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BOWImgDescriptorExtractor
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-------------------------
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.. c:type:: BOWImgDescriptorExtractor
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Class to compute image descriptor using ''bad of visual words''. In few,
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such computing consists from the following steps:
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1. Compute descriptors for given image and it's keypoints set,
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\
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2. Find nearest visual words from vocabulary for each keypoint descriptor,
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\
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3. Image descriptor is a normalized histogram of vocabulary words encountered in the image. I.e.
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``i`` -bin of the histogram is a frequency of ``i`` -word of vocabulary in the given image. ::
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class BOWImgDescriptorExtractor
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{
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public:
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BOWImgDescriptorExtractor( const Ptr<DescriptorExtractor>& dextractor,
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const Ptr<DescriptorMatcher>& dmatcher );
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virtual ~BOWImgDescriptorExtractor(){}
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void setVocabulary( const Mat& vocabulary );
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const Mat& getVocabulary() const;
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void compute( const Mat& image, vector<KeyPoint>& keypoints,
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Mat& imgDescriptor,
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vector<vector<int> >* pointIdxsOfClusters=0,
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Mat* descriptors=0 );
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int descriptorSize() const;
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int descriptorType() const;
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protected:
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...
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};
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..
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.. index:: BOWImgDescriptorExtractor::BOWImgDescriptorExtractor
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BOWImgDescriptorExtractor::BOWImgDescriptorExtractor
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--------------------------------------------------------
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.. c:function:: BOWImgDescriptorExtractor::BOWImgDescriptorExtractor( const Ptr<DescriptorExtractor>\& dextractor, const Ptr<DescriptorMatcher>\& dmatcher )
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Constructor.
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:param dextractor: Descriptor extractor that will be used to compute descriptors
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for input image and it's keypoints.
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:param dmatcher: Descriptor matcher that will be used to find nearest word of trained vocabulary to
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each keupoints descriptor of the image.
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.. index:: BOWImgDescriptorExtractor::setVocabulary
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BOWImgDescriptorExtractor::setVocabulary
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--------------------------------------------
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.. c:function:: void BOWImgDescriptorExtractor::setVocabulary( const Mat\& vocabulary )
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Method to set visual vocabulary.
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:param vocabulary: Vocabulary (can be trained using inheritor of :func:`BOWTrainer` ).
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Each row of vocabulary is a one visual word (cluster center).
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.. index:: BOWImgDescriptorExtractor::getVocabulary
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BOWImgDescriptorExtractor::getVocabulary
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--------------------------------------------
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.. c:function:: const Mat\& BOWImgDescriptorExtractor::getVocabulary() const
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Returns set vocabulary.
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.. index:: BOWImgDescriptorExtractor::compute
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BOWImgDescriptorExtractor::compute
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--------------------------------------
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.. c:function:: void BOWImgDescriptorExtractor::compute( const Mat\& image, vector<KeyPoint>\& keypoints, Mat\& imgDescriptor, vector<vector<int> >* pointIdxsOfClusters=0, Mat* descriptors=0 )
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Compute image descriptor using set visual vocabulary.
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:param image: The image. Image descriptor will be computed for this.
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:param keypoints: Keypoints detected in the input image.
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:param imgDescriptor: This is output, i.e. computed image descriptor.
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:param pointIdxsOfClusters: Indices of keypoints which belong to the cluster, i.e. ``pointIdxsOfClusters[i]`` is keypoint indices which belong
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to the ``i-`` cluster (word of vocabulary) (returned if it is not 0.)
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:param descriptors: Descriptors of the image keypoints (returned if it is not 0.)
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.. index:: BOWImgDescriptorExtractor::descriptorSize
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BOWImgDescriptorExtractor::descriptorSize
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---------------------------------------------
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.. c:function:: int BOWImgDescriptorExtractor::descriptorSize() const
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Returns image discriptor size, if vocabulary was set, and 0 otherwise.
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.. index:: BOWImgDescriptorExtractor::descriptorType
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BOWImgDescriptorExtractor::descriptorType
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---------------------------------------------
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.. c:function:: int BOWImgDescriptorExtractor::descriptorType() const
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Returns image descriptor type.
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