How to classify people's clothes by Gabor filter?

Question

I'd like to identify person from another using Gabor filter.

It is working fine but I don't understand how to classify.

Does it need for example to SVM as classifier?

I understand from this paper that it don't need SVM OR another classifier

The full code in real-time(video):

import cv2
import numpy as np
from imutils.video import FPS
# capturing video through webcam
import time
cap = cv2.VideoCapture(0)
#video dimension in python-opencv
width = cap.get(3)  # float
height = cap.get(4) # float
print width,height
time.sleep(2.0)
fps = FPS().start()
while(1):
    _, img = cap.read()


    if _ is True:
        img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        # img =cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    else:
        continue
    g_kernel = cv2.getGaborKernel((21, 21), 8.0, np.pi / 4, 10.0, 0.5, 0, ktype=cv2.CV_32F)
    # print g_kernel
    filtered_img = cv2.filter2D(img, cv2.CV_8UC3, g_kernel)
    # print filtered_img
    # kernel_resized = cv2.resize(g_kernel)




    cv2.imshow("Original Tracking", img)
    cv2.imshow("Color Tracking", filtered_img)
    h, w = g_kernel.shape[:2]
    g_kernel = cv2.resize(g_kernel, (3 * w, 3 * h), interpolation=cv2.INTER_CUBIC)
    cv2.imshow('gabor kernel (resized)', g_kernel)
    # cv2.imshow("kernel", g_kernel)
    if cv2.waitKey(10) & 0xFF == ord('q'):
        cap.release()
        cv2.destroyAllWindows()
        break
    fps.update()
    fps.stop()
    print("[INFO] elapsed time: {:.2f}".format(fps.elapsed()))
    print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))

please help me

Thank you in advance.