文件名称:bag_words_demo
- 所属分类：
- 人工智能/神经网络/遗传算法
- 资源属性：
- [Matlab] [源码]
- 上传时间：
- 2012-11-26
- 文件大小：
- 897kb
- 下载次数：
- 0次
- 提 供 者：
- 张**
- 相关连接：
- 无
- 下载说明：
- 别用迅雷下载，失败请重下，重下不扣分！
介绍说明－－下载内容均来自于网络，请自行研究使用
一个学习自然场景类别的贝叶斯模型、基于“词袋”模型的目标分类。来源于Feifei Li的论文。是近年来的目标识别模型热点之一。-”A Bayesian Hierarchical Model for Learning Natural Scene Categories“ FeiFei Li.CVPR2005
相关搜索： 目标识别
a
bayesian
hierarchical
model
for
learning
natural
bag_words_demo
zip
bag_words_demo
bag
wor
a
bayesian
hierarchical
model
for
learning
natural
bag_words_demo
zip
bag_words_demo
bag
wor
(系统自动生成,下载前可以参看下载内容)
下载文件列表
common
......\clear_model.m
......\compute_descriptors.ln
......\discrete_sampler.m
......\dist_transform.m
......\dist_transform_1d.m
......\do_all.m
......\do_form_codebook.m
......\do_interest_operator.m
......\do_manual_train_parts_structure.m
......\do_naive_bayes.m
......\do_naive_bayes_evaluation.m
......\do_part_filtering.m
......\do_plsa.m
......\do_plsa_evaluation.m
......\do_preprocessing.m
......\do_random_indices.m
......\do_representation.m
......\do_test_efficient.m
......\do_test_parts_structure.m
......\do_vq.m
......\drawcircle.m
......\draw_ellipse.m
......\Edge_Sampling.m
......\genFileNames.m
......\get_new_model_name.m
......\gg_lola_km_binary.m
......\localmax.m
......\Norm_Corr.m
......\pLSA_EM.m
......\prefZeros.m
......\recall_precision_curve.m
......\roc.m
......\SIFT.m
......\test_localization.m
......\vgg_argparse.m
......\vgg_kmeans.m
......\vgg_kmiter.cxx
......\vgg_kmiter.mexglx
......\vgg_nearest_neighbour.cxx
......\vgg_nearest_neighbour.m
......\vgg_nearest_neighbour.mexglx
......\vgg_save_image.m
......\vgg_save_image_mit.m
......\vgg_save_image_pgm.m
......\vgg_save_image_ppm.m
......\vgg_xcv_segment.m
......\xcv_segment
......\xcv_segment.exe
experiments
...........\bag_of_words
...........\............\config_file_1.m
...........\............\config_file_2.m
......\clear_model.m
......\compute_descriptors.ln
......\discrete_sampler.m
......\dist_transform.m
......\dist_transform_1d.m
......\do_all.m
......\do_form_codebook.m
......\do_interest_operator.m
......\do_manual_train_parts_structure.m
......\do_naive_bayes.m
......\do_naive_bayes_evaluation.m
......\do_part_filtering.m
......\do_plsa.m
......\do_plsa_evaluation.m
......\do_preprocessing.m
......\do_random_indices.m
......\do_representation.m
......\do_test_efficient.m
......\do_test_parts_structure.m
......\do_vq.m
......\drawcircle.m
......\draw_ellipse.m
......\Edge_Sampling.m
......\genFileNames.m
......\get_new_model_name.m
......\gg_lola_km_binary.m
......\localmax.m
......\Norm_Corr.m
......\pLSA_EM.m
......\prefZeros.m
......\recall_precision_curve.m
......\roc.m
......\SIFT.m
......\test_localization.m
......\vgg_argparse.m
......\vgg_kmeans.m
......\vgg_kmiter.cxx
......\vgg_kmiter.mexglx
......\vgg_nearest_neighbour.cxx
......\vgg_nearest_neighbour.m
......\vgg_nearest_neighbour.mexglx
......\vgg_save_image.m
......\vgg_save_image_mit.m
......\vgg_save_image_pgm.m
......\vgg_save_image_ppm.m
......\vgg_xcv_segment.m
......\xcv_segment
......\xcv_segment.exe
experiments
...........\bag_of_words
...........\............\config_file_1.m
...........\............\config_file_2.m