文件列表:

images\p1010843.jpg (50221, 2012-02-29)
images\p1010844.jpg (27646, 2012-02-29)
images\p1010845.jpg (34972, 2012-02-29)
images\p1010846.jpg (25299, 2012-02-29)
images\p1010847.jpg (29458, 2012-02-29)
BuildHistograms.m (5016, 2012-02-29)
BuildPyramid.m (3607, 2012-02-29)
CalculateDictionary.m (4856, 2012-02-29)
CompilePyramid.m (5309, 2012-02-29)
Example.m (1141, 2012-02-29)
GenerateSiftDescriptors.m (2675, 2012-02-29)
hist_isect.m (759, 2012-02-29)
hist_isect_c.c (2224, 2012-02-29)
sp_dense_sift.m (4103, 2012-02-29)
sp_dist2.m (979, 2012-02-29)
sp_find_grid.m (449, 2012-02-29)
sp_find_sift_grid.m (4187, 2012-02-29)
sp_gen_sift.m (1675, 2012-02-29)
sp_kmeans.m (3758, 2012-02-29)
sp_load_image.m (154, 2012-02-29)
sp_make_dir.m (223, 2012-02-29)
sp_normalize_sift.m (650, 2012-02-29)
sp_progress_bar.m (1464, 2012-02-29)

======================================================================== Spatial Pyramid Code Created by Joe Tighe (jtighe@cs.unc.edu) and Svetlana Lazebnik (lazebnik@cs.unc.edu) 1/17/2009 This MATLAB code implements spatial pyramid computation and matching as described in the following paper: S. Lazebnik, C. Schmid, and J. Ponce, ``Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories,'' CVPR 2006. ======================================================================== 2/29/2012: -Updated sift normalization. Previous update normalized sift across all values causeing sift descriptors in flat regions to effectly be noise.(see sp_gen_sift.m for details) 9/3/2010: -Upadated sift generation to allow for fast generation of sift descriptor at every pixel (see sp_gen_sift.m for details). -Added progress bars for user feedback -Slight change to interface to allow for clearner code. See Example.m for usage. The main function to build the spatial pyramid is BuildPyramid. For further information on Buildpyramid and other functions discussed in this file refer to comments in the .m files or look at Example.m. (The images/ directory contains a few sample images that are used by Example.m to compute spatial pyramids and their histogram intersection matrix.) BuildPyramid first extracts SIFT descriptors on a regular grid from each image. It then runs k-means to find the dictionary. Each sift descriptor is given a texton label corresponding to the closest dictionary codeword. Finally, the spatial pyramid is generated from these labels. Each of these steps are split up into individual functions and can be called independently, provided the data from the previous step is stored in the correct location. The functions are as follows: GenerateSiftDescriptors CalculateDictionary BuildHistograms CompilePyramid If you wish to use one of these functions without first running the previous step, you will need to provide the appropriate data files. They should be in the dataBaseDir with the same relative path as the image they correspond to. Their file names should be the same as the image they correspond to with the appropriate suffix appended to the end. For instance if you call CalculateDictionary with featureSuffix = ‘_sift.mat’ CalculateDictionary will look for the data file ‘dataBaseDir/im1_sift.mat’ for the image file ‘imageBaseDir/im1.jpg’. There are two different types of data files (feature lists and texton indices). Each must be formatted correctly to work with the functions provided. features: data: NxM matrix of image features where N is the number of features in the image and M is the feature vector size x: Nx1 vector of image x coordinates where N is the number of features in the image y: Nx1 vector of image y coordinates where N is the number of features in the image wid: width of the image hgt: height of the image texton_ind: data: Nx1 vector of texton indices corresponding to the appropriate dictionary bin for each feature of the image where N is the number of features in the image x: Nx1 vector of image x coordinates where N is the number of features in the image y: Nx1 vector of image y coordinates where N is the number of features in the image wid: width of the image hgt: height of the image NOTE: This code does not include functionality for SVM classification, though it does include functions for computing the histogram intersection kernel matrix (hist_isect.m and hist_isect_c.c). For classification, we have used the svm_v0.55 MATLAB toolbox: http://theoval.sys.uea.ac.uk/~gcc/svm/toolbox However, any other SVM package (and kernels other than histogram intersection) can be adapted.

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