'mlda' Package

The mlda enables the object categorization using MLDA.

• Maintainer: Shoichi Hasegawa ([email protected]).
• Author: Shoichi Hasegawa ([email protected]).

Content:

• Requirements
• Launch
• References

Requirement

Python2.X and the following modules are required

import numpy as np
import random
import math
import pylab
import pickle
import os
import sys
import cv2
import glob
import codecs

apt-get install python_tk

Launch

1. Write sentences in /MLDA/bow/text.txt
2. Create BoW:python /MLDA/bow/bow.py
3. Create BoF:python /MLDA/bof/bof.py
4. python /MLDA/mlda.py

• /MLDA/mlda.py:MLDA by using Collapsed Gibbs sampler with Python.You need to decide the number of topic.

def main():
    topic = 3
    data = []
    data.append( np.loadtxt( "./bof/histogram_v.txt" , dtype=np.int32) )
    data.append( np.loadtxt( "./bow/histogram_w.txt" , dtype=np.int32)*5 )
    mlda( data, topic, 100, "learn_result" )

    data[1] = None
    mlda( data, topic, 10, "recog_result" , "learn_result" )

※Before running this script, you need to run /MLDA/bow/bow.py and /MLDA/bof/bof.py which create BoW and BoF file

• /MLDA/bow/bow.py: It can generate BoW file which is used by /MLDA/mlda.py from /MLDA/bow/text.txt

• /MLDA/bow/text.txt: You can write sentences.
  ※Sentences must be separated by spaces for each word.

  • For example

• /MLDA/bof/bof.py: It can generate BoF file which is used by /MLDA/mlda.py from /MLDA/bof/images/*.png

References

• Implementions of LDAs: https://github.com/is0383kk/LDAs
• Multimodal Object Dataset 165: https://hp.naka-lab.org/subpages/mod165.html
• MLDA: https://github.com/naka-tomo/MLDA-PY