Implementation of KNN method for object recognition.

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Implementation of KNN method for object
recognition.

• Presented by Natalya Chilina

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Outline

• Introduction.
• Description of the problem.
• Description of the method.
• Image library.
• Process of identification.
• Example.
• Future work.

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Introduction

• Generally speaking, problem of object recognition
  is how to teach computer to recognize different
  objects on a picture.
• This is a nontrivial problem. Some of the main
  difficulties in solving this problem are
  separation of an object from the background
  (especially in the presence of clutter or
  occlusions in the background), and ability to
  recognize an object with different lighting.

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Introduction.

• In this research I am trying to improve accuracy
  of object recognition by implementation of KNN
  method with new weighted Hamming-Levenshtein
  distance that I developed.

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Description of the problem.

• The problem of object recognition can be divided
  into two parts
• 1) Location of an object on the picture
• 2) Identification of an object.
• For example, assume that we have the following
  picture

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Description of the problem.
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Description of the problem.

• and we have the following library of images that
  we will use for object identification

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Description of the problem.

• Our goal is to identify and locate objects from
  our library on the picture.

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Description of the problem.

• In this research I have developed a method of
  objects identification assuming that we already
  know the location of an object, and I am going to
  develop the method of location in my future work.

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Description of the method.

• We will use KNN method to identify objects.
• For example, assume we need to identify an object
  X on a given picture. Let us consider the space
  of pictures generated by the image of X and
  images from our library.

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Description of the method.

• In this space we will pick up, say 5, closest to
  X images, and identify X by finding the plurality
  class of the nearest pictures.

C1
A3
B3
A1
X
A2
B2
B1
C2
Nearest neighbors A1, B1, A2, B2, A3
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Description of the method.

• In order to use KNN method we need to introduce a
  measure of similarity between two pictures.
• First of all, in order to say something about
  similarity between pictures, we need to get some
  ideas about the shape of objects on these
  pictures. To do this we use edge-detection method
  (Sobel method, for example).

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Description of the method.

• Next, we turn the edge-detected picture into a
  bit array by thresholding intensities to 0 or 1.
  In fact, we are going to keep images in the
  library in this form.

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Description of the method.

• Now, in order to compare two pictures , we need
  to compare two 2-dimensional bit arrays.
• It may seem natural to use the traditional
  Hamming distance for bitstrings which is defined
  as follows given two bitstrings of the same
  dimension, the Hamming distance is the minimum
  number of symbol changes needed to change one
  bitmap into the other.

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Description of the method.

• For example, the Hamming distance between
• (A) 10001001 and
• (B) 11100000 is 4.
• Notice that the Hamming distance between
• (A) 10001001 and
• (C) 10010010 is also 4, but intuitively one can
  regard (C) as a better match for (A) than (B).

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Description of the method.

• We can modify Hamming distance using the idea of
  Levenshtein distance which is usually used for
  comparing of text strings and is obtained by
  finding the cheapest way to transform one string
  into another. Transformations are the one-step
  operations of insertion, deletion and
  substitution, and each transformation has a
  certain cost.

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Description of the method.

• Also, since different parts of images have
  different level of importance in the process of
  recognition, we can assign a weight value for
  each pixel of an image, and use it in the
  definition of a distance. For example, we can
  eliminate the background of a picture by
  assigning to the corresponding pixels zero
  weight.

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Description of the method.

• To get weighted Hamming-Levenshtein distance
  between two pictures we
• divide each bitstring into several substrings of
  the same length.
• Then we compare corresponding substrings using
  Levenshtein distance,
• And summarize all these distances multiplied by
  the average weight of each substring.

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Image library.

• Each object in the library is represented by
  several images taken with different lighting and
  from different sides. Each image in the library
  is represented by two 2-dimensional arrays. First
  array contains the edge-detected picture turned
  into a bit array, and the second one contains
  weight values assigned to each pixel.

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Process of identification.

• To identify an object,
• we turn its edge-detected image into a bit array
  by thresholding intensities to 0 or 1.
• Then we measure distance between this image and
  each image from our library using corresponding
  weight arrays and weighted Hamming-Levenshtein
  distance.
• Using KNN method we identify the object.

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Example.

• Below I present some results in object
  identification that I obtained using the method
  that I described.

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Example.

• Assume that we have the image library with the
  following edge-detected images of objects and
  weighted images.

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Example.
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Example.

• I want to identify objects from this picture.

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Example.

• Let us try to identify the following picture.

Picture 1
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Example.

• We compare this picture with each image in our
  library, and we get the following table of
  distances.

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Example.

• If we select three closest neighbors of our
  picture 1, then we can identify it as Bear.

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Example.

• Let us do similar calculations for these two
  pictures

Picture 3.
Picture 2.
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Future work.

• Develop a method of location of an object on the
  picture.
• Develop an idea of reasonable weight distribution
  on the images from the library.
• Improve the algorithm of identification to allow
  to compare pictures of different sizes.
• Continue to work on improving the definition of
  weighted Hamming-Levenshtein distance.

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References.

• A. Bookstein, V. Kulyukin, T. Raita (2002)
  Generalized Hamming Distance.
• Michael Gilleland, Merriam Park Software
  Levenshtein Distance, in Three Flavors.
• Bill Green (2002) Edge Detection Tutorial
• V. Kulyukin (2004) Human-Robot Interaction
  Through Gesture-Free Spoken Dialogue.

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• Thank you!