Information Retrieved           - Image based search     s1160123 Tomoyuki Soeta  Supervised by Prof. Qiangfu Zhao      Sy...
Outline   Introduction   Information Retrieval   VQ (Vector Quantization)   Divide into the 8x8 block   Making of Cod...
Introduction   I want to aim at the improvement of information retrieval    system to search it even if the input data ar...
Information Retrieved                                     Image     Text                            Divide into the block ...
VQ (Vector Quantization)                     Compression coding of images                     Image compression technolo...
Divide into the 8x8 block (1)   I used 10 facial images with the size 256x256.   images are converted to gray scale imag...
Divide into the 8x8 block (2)             Block’s pixel value is read.             Pixel read value is stored in the arr...
10 imagesImage 0   Image 1   Image 2   Image 3   Image 4Image 5   Image 6   Image 7   Image 8   Image 9                   ...
Making of Code book The  array of 10240 that can be done  by reading 10 images is made The code book is made by using th...
K-means algorithmStep 1) k initial "means" are randomly selected fromthe data set .Step 2) k clusters are created by assoc...
Extract each image’s feature vector (1)           The feature vector are extracted by using code book.           There i...
Result – image and feature vector(1)    Image 0              Image 1                                   12
Result – image and feature vector(2)    Image 2              Image 3                                   13
Result – image and feature vector(3)    Image 4              Image 5                                   14
Result – image and feature vector(4)    Image 6              Image 7                                   15
Result – image and feature vector(5)    Image 8              Image 9                                   16
Result - Distance of feature vector(1)         Euclidean distance between feature vectors          is measured, and the a...
Result - Distance of feature vector(2)      256 feature0    feature1       feature2    feature3    feature4    feature5   ...
Conclusion   In my research, I study a new image search    technique based on the code book    information. The code book...
Future work   The background is nullified.   The feature vector is extracted in the block    of a different size like th...
Thank you for your attention!                                21
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Thesis writing - week9

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Tomoyuki Soeta

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Thesis writing - week9

  1. 1. Information Retrieved - Image based search s1160123 Tomoyuki Soeta Supervised by Prof. Qiangfu Zhao System Intelligence Lab 1
  2. 2. Outline Introduction Information Retrieval VQ (Vector Quantization) Divide into the 8x8 block Making of Code book K-means algorithm Extract each image’s feature vector Result  Image and feature vector  Distance of feature vector Conclusion Future work 2
  3. 3. Introduction I want to aim at the improvement of information retrieval system to search it even if the input data are documents or images. I have charge of a research on information retrieval based on a image. To search images using a search engine, we may use the index attached to the image, the file name, etc. as the key-words. We may also use "the contents of an image themselves." I study a new image search technique based on the code book information. 3
  4. 4. Information Retrieved Image Text Divide into the block (1 block 8x8) Morphological Analysis Code bookWord Filtering Code of each block Feature Vector Feature Vector NNTree or SVM 4
  5. 5. VQ (Vector Quantization)  Compression coding of images  Image compression technologyimage  In my study, I use VQ to Vector translate an image into a bag- Quantization of-blocks (BOB) (VQ) same way as document search 5 feature vector
  6. 6. Divide into the 8x8 block (1) I used 10 facial images with the size 256x256. images are converted to gray scale images. Divided into the block (one-block 8x8 size). Each image obtains the block of 32×32 pieces severally. 32 blocks 32 b l 1 block 8x8 o c k s 6
  7. 7. Divide into the 8x8 block (2)  Block’s pixel value is read.  Pixel read value is stored in the array of 1x64.  One image can be divided into 1024 blocks, and an array of 1024 rows can be obtained. 1x64 2 3 4 6 8 3 7 2 2 3 4 6 8 3 7 2 8 2 8 2 8 2 8 2 8 21 block 8x8 ・ ・ ・ ・ 8x8 1024 rows 7
  8. 8. 10 imagesImage 0 Image 1 Image 2 Image 3 Image 4Image 5 Image 6 Image 7 Image 8 Image 9 8
  9. 9. Making of Code book The array of 10240 that can be done by reading 10 images is made The code book is made by using the k-means method. Making Code book (size 256) 9
  10. 10. K-means algorithmStep 1) k initial "means" are randomly selected fromthe data set .Step 2) k clusters are created by associating everyobservation with the nearest mean.Step 3) The centroid of each of the k clustersbecomes the new means.Step 4) Steps 2 and 3 are repeated untilconvergence has been reached.Step 1 Step 2 Step 3 Step 4 10
  11. 11. Extract each image’s feature vector (1)  The feature vector are extracted by using code book.  There is arrangement 1024 per one image.  Arranging an individual distance of the array each one and code book is measured  The number of the nearest code is returned.  Which code how many times came out is preserved as an array. 5 1x64 42 3 4 7 8 9 2 ########## Code 7 3 Code 38 Code 72 2 Code 200 1 Code 7 Code 200 Code 72 Code 38 7 0 ・ 1 256 ・ 7 38 72 200 ・ 1024 rows Code book 11
  12. 12. Result – image and feature vector(1) Image 0 Image 1 12
  13. 13. Result – image and feature vector(2) Image 2 Image 3 13
  14. 14. Result – image and feature vector(3) Image 4 Image 5 14
  15. 15. Result – image and feature vector(4) Image 6 Image 7 15
  16. 16. Result – image and feature vector(5) Image 8 Image 9 16
  17. 17. Result - Distance of feature vector(1)  Euclidean distance between feature vectors is measured, and the accuracy of the code book is seen. P and Q are assumed to be two feature vectors. Data : x = (x1, x2, ..., xn) and y = (y1, y2, ..., yn) n : size of the feature vector The distance of P and Q is below. 17
  18. 18. Result - Distance of feature vector(2) 256 feature0 feature1 feature2 feature3 feature4 feature5 feature6 feature7 feature8 feature9feature0 0 0.279945 0.280761 0.226158 0.291376 0.322875 0.300502 0.2307 0.23509 0.228708feature1 0.279945 0 0.19849 0.271927 0.318353 0.352126 0.324807 0.272823 0.269333 0.30847feature2 0.280761 0.19849 0 0.308124 0.352732 0.378846 0.359333 0.310492 0.316141 0.324054feature3 0.226158 0.271927 0.308124 0 0.221109 0.276269 0.240734 0.09959 0.086469 0.136439feature4 0.291376 0.318353 0.352732 0.221109 0 0.222279 0.17478 0.202749 0.210865 0.248531feature5 0.322875 0.352126 0.378846 0.276269 0.222279 0 0.084866 0.282603 0.270858 0.306136feature6 0.300502 0.324807 0.359333 0.240734 0.17478 0.084866 0 0.245255 0.232873 0.276931feature7 0.2307 0.272823 0.310492 0.09959 0.202749 0.282603 0.245255 0 0.105974 0.155957feature8 0.23509 0.269333 0.316141 0.086469 0.210865 0.270858 0.232873 0.105974 0 0.152093feature9 0.228708 0.30847 0.324054 0.136439 0.248531 0.306136 0.276931 0.155957 0.152093 0 :minimum distance  The image5 and image6 is the same persons, image5 doesnt wear glasses, and image6 wears glasses.  Between feature5 and feature6 is minimum distance.Image 5 Image 6 18
  19. 19. Conclusion In my research, I study a new image search technique based on the code book information. The code book is obtained using the VQ method. It is thought that an accurate feature vector was able to be extracted about the accuracy of the feature vector because the distance between Feature5 and 6 was short. Information retrieval based on "the contents of a image themselves." 19
  20. 20. Future work The background is nullified. The feature vector is extracted in the block of a different size like the block of not the block of 8x8 size but 16x16 size etc. Multimedia retrieval that uses SVM. 20
  21. 21. Thank you for your attention! 21

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