Row 65194

Row ID: 65194 | Dataset Entry | Axioma AXP Content Repository

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Hi everyone, Currently, i'm processing a image classification problem about facial emotional classification. I am using 2 extract methods: HOG and Facial Landmark. My idea is using HOG to find the gradient magnitude and oriented of the image and use facial landmark to find face keypoint. I thought i can fuse 2 method to make a better feature. But the new feature worse than HOG and better than facial landmark (same model to evaluate). I have some question:

1. I wonder how i can fuse these two method where HOG normalization before and facial landmark return 68x2 pairs point integer. 2. If can, should i normalize or something before fuse ? Which method i can try to fuse them (concat, add, multiply, ...) ? 3. Is there anyway how to measure my method will be better or evaluate it ? I am also try to fuse HOG and SIFT (Bag of visual word) too.

I had tried fuse HOG and Facial Landmark feature but it get worse than HOG and better than Facial Landmark in the same model. I also fuse (SIFT) bag of visual word and HOG but it still worse than HOG and better than bag of visual word. Here is the code i use:

x_hogp_train = pca.transform(x_hog_train)[:,:382] x_hogp_valid = pca.transform(x_hog_valid)[:,:382] x_hogp_test = pca.transform(x_hog_test)[:,:382] scaler = StandardScaler() # scale bovw feature scaler.fit(x_bovw_train) x_scale_bovw_train = scaler.transform(x_bovw_train) x_scale_bovw_valid = scaler.transform(x_bovw_valid) x_scale_bovw_test = scaler.transform(x_bovw_test) # fuse them use concat x_fused_train = np.concatenate((x_hogp_train, x_scale_bovw_train), axis=1) x_fused_valid = np.concatenate((x_hogp_valid, x_scale_bovw_valid), axis=1) x_fused_test = np.concatenate((x_hogp_test, x_scale_bovw_test), axis=1) Thank in advance

Field	Value
text	Hi everyone, Currently, i'm processing a image classification problem about facial emotional classification. I am using 2 extract methods: HOG and Facial Landmark. My idea is using HOG to find the gradient magnitude and oriented of the image and use facial landmark to find face keypoint. I thought i can fuse 2 method to make a better feature. But the new feature worse than HOG and better than facial landmark (same model to evaluate). I have some question: 1. I wonder how i can fuse these two …
label	r/machinelearning
dataType	post
communityName	r/MachineLearning
datetime	2024-05-23
username_encoded	Z0FBQUFBQm5Lak1ieGhkdlpGajYzVU93VUZJMnhiSlR6RXBaQTZtWE5kNnpuQm1meDhkdDRBZlI1LUU4UnFRdGxCemJ4cDFVRGFXUGp5YUcyRFphZzU3Z2tHZ1NXYV9kVXIxZnU0ZVBSZjViamI2TUxWenk2Tm89
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Raw Record

{
  "text": "Hi everyone,  \nCurrently, i'm processing a image classification problem about facial emotional classification. I am using 2 extract methods: HOG and Facial Landmark. My idea is using HOG to find the gradient magnitude and oriented of the image and use facial landmark to find face keypoint. I thought i can fuse 2 method to make a better feature. But the new feature worse than HOG and better than facial landmark (same model to evaluate). I have some question:\n\n1. I wonder how i can fuse these two method where HOG normalization before and facial landmark return 68x2 pairs point integer.\n2. If can, should i normalize or something before fuse ? Which method i can try to fuse them (concat, add, multiply, ...) ?\n3. Is there anyway how to measure my method will be better or evaluate it ? I am also try to fuse HOG and SIFT (Bag of visual word) too.\n\nI had tried fuse HOG and Facial Landmark feature but it get worse than HOG and better than Facial Landmark in the same model. I also fuse (SIFT) bag of visual word and HOG but it still worse than HOG and better than bag of visual word. Here is the code i use:\n\n    x_hogp_train = pca.transform(x_hog_train)[:,:382]\n    x_hogp_valid = pca.transform(x_hog_valid)[:,:382]\n    x_hogp_test = pca.transform(x_hog_test)[:,:382]\n    \n    scaler = StandardScaler() # scale bovw feature\n    scaler.fit(x_bovw_train)\n    x_scale_bovw_train = scaler.transform(x_bovw_train)\n    x_scale_bovw_valid = scaler.transform(x_bovw_valid)\n    x_scale_bovw_test = scaler.transform(x_bovw_test)\n    \n    # fuse them use concat\n    x_fused_train = np.concatenate((x_hogp_train, x_scale_bovw_train), axis=1)\n    x_fused_valid = np.concatenate((x_hogp_valid, x_scale_bovw_valid), axis=1)\n    x_fused_test = np.concatenate((x_hogp_test, x_scale_bovw_test), axis=1)\n    \n    Thank in advance",
  "label": "r/machinelearning",
  "dataType": "post",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-23",
  "username_encoded": "Z0FBQUFBQm5Lak1ieGhkdlpGajYzVU93VUZJMnhiSlR6RXBaQTZtWE5kNnpuQm1meDhkdDRBZlI1LUU4UnFRdGxCemJ4cDFVRGFXUGp5YUcyRFphZzU3Z2tHZ1NXYV9kVXIxZnU0ZVBSZjViamI2TUxWenk2Tm89",
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}

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Entry Information

Entry ID: 65194
Repository: Axioma AXP
Dataset: arrmlet/reddit_dataset_36
Total Entries: 100,000