bag = bagOfFeatures(trainingSet);

以下是前面的运行结果，你可以检查一下是否有出入，数据集有没有正确加载？另外这些代码是在matlab2015b中编写的

ans = Label Count __ _____

Faces 435 Faces_easy 435 Leopards 200 Motorbikes 798 accordion 55

ans = Label Count __ _____

Faces 31 Faces_easy 31 Leopards 31 Motorbikes 31 accordion 31

Creating Bag-Of-Features.

Image category 1: Faces
Image category 2: Faces_easy
Image category 3: Leopards
Image category 4: Motorbikes
Image category 5: accordion
Image category 6: airplanes
Image category 7: anchor
Image category 8: ant
Image category 9: barrel
Image category 10: bass
Image category 11: beaver
Image category 12: binocular
Image category 13: bonsai
Image category 14: brain
Image category 15: brontosaurus
Image category 16: buddha
Image category 17: butterfly
Image category 18: camera
Image category 19: cannon
Image category 20: car_side
Image category 21: ceiling_fan
Image category 22: cellphone
Image category 23: chair
Image category 24: chandelier
Image category 25: cougar_body
Image category 26: cougar_face
Image category 27: crab
Image category 28: crayfish
Image category 29: crocodile
Image category 30: crocodile_head
Image category 31: cup
Image category 32: dalmatian
Image category 33: dollar_bill
Image category 34: dolphin
Image category 35: dragonfly
Image category 36: electric_guitar
Image category 37: elephant
Image category 38: emu
Image category 39: euphonium
Image category 40: ewer
Image category 41: ferry
Image category 42: flamingo
Image category 43: flamingo_head
Image category 44: garfield
Image category 45: gerenuk
Image category 46: gramophone
Image category 47: grand_piano
Image category 48: hawksbill
Image category 49: headphone
Image category 50: hedgehog
Image category 51: helicopter
Image category 52: ibis
Image category 53: inline_skate
Image category 54: joshua_tree
Image category 55: kangaroo
Image category 56: ketch
Image category 57: lamp
Image category 58: laptop
Image category 59: llama
Image category 60: lobster
Image category 61: lotus
Image category 62: mandolin
Image category 63: mayfly
Image category 64: menorah
Image category 65: metronome
Image category 66: minaret
Image category 67: nautilus
Image category 68: octopus
Image category 69: okapi
Image category 70: pagoda
Image category 71: panda
Image category 72: pigeon
Image category 73: pizza
Image category 74: platypus
Image category 75: pyramid
Image category 76: revolver
Image category 77: rhino
Image category 78: rooster
Image category 79: saxophone
Image category 80: schooner
Image category 81: scissors
Image category 82: scorpion
Image category 83: sea_horse
Image category 84: snoopy
Image category 85: soccer_ball
Image category 86: stapler
Image category 87: starfish
Image category 88: stegosaurus
Image category 89: stop_sign
Image category 90: strawberry
Image category 91: sunflower
Image category 92: tick
Image category 93: trilobite
Image category 94: umbrella
Image category 95: watch
Image category 96: water_lilly
Image category 97: wheelchair
Image category 98: wild_cat
Image category 99: windsor_chair
Image category 100: wrench
Image category 101: yin_yang
Selecting feature point locations using the Grid method.
Extracting SURF features from the selected feature point locations. ** The GridStep is [8 8] and the BlockWidth is [32 64 96 128].
Extracting features from 909 images...done. Extracted 4005040 features.
Keeping 80 percent of the strongest features from each category.
Balancing the number of features across all image categories to improve clustering. Image category 3 has the least number of strongest features: 11059. Using the strongest 11059 features from each of the other image categories.
Using K-Means clustering to create a 500 word visual vocabulary.
Number of features : 1116959
Number of clusters (K) : 500
Initializing cluster centers...0.40%100.00%.

chengzhag / matlab_image_classification

bag = bagOfFeatures(trainingSet); #1

Creating Bag-Of-Features.