Apparatus and method for processing image based on layers

• US 20060115162A1
• Filed: 06/06/2005
• Published: 06/01/2006
• Est. Priority Date: 11/26/2004
• Status: Abandoned Application

• Current Assignee: Samsung Electronics Co. Ltd.
• Original Assignee: Samsung Electronics Co. Ltd.
• Inventors: Kee, Seokcheol, Park, Chanmin, Hwang, Wonjun
• Application Number:
  US11/145,178
• Publication Number:
  US 20060115162A1
• Time in Patent Office: Days
• US Class Current: 382/205
• CPC Class Codes: G06V 40/172 Classification, e.g. identification

Abstract

An apparatus and method for processing an image based on layers. The apparatus includes: an image divider dividing an image into E layers, each layer having at least one block, e being an positive integer at least equal to 2; and first through E-th layer basis matrix generators respectively generating first through E-th layer basis matrices using the divided image and outputting a set of the first through E-th layer basis matrices as a final basis matrix, wherein the e-th (1≦e≦E) layer basis matrix generator, with respect to each block included in the e-th layer, generates a block model using a kernel matrix obtained by local feature analysis, multiplies a zero mean matrix generated from the divided image by the result of transposing the block model, calculates a between-class scatter matrix and a within-class scatter matrix by linear discriminant analysis using the multiplied result, calculates a discriminant transformation matrix using the calculated between-class scatter matrix and the calculated within-class scatter matrix, multiplies the discriminant transformation matrix by the block model, outputs the multiplied result as a subbasis matrix, and outputs a set of subbasis matrices generated in all of the blocks included in the e-th layer as the e-th layer basis matrix.

Claims

1. An apparatus for processing an image based on layers, the apparatus comprising:

an image divider dividing the image into E layers, each layer having at least one block, E being a positive integer at least equal to 2; and

first through E-th layer basis matrix generators respectively generating first through E-th layer basis matrices using the divided image and outputting a set of the first through E-th layer basis matrices as a final basis matrix,

wherein the e-th (1≦

e≦

E) layer basis matrix generator, with respect to each block included in the e-th layer, generates a block model using a kernel matrix obtained by local feature analysis, multiplies a zero mean matrix generated from the divided image by a result of transposing the block model, calculates a between-class scatter matrix and a within-class scatter matrix by linear discriminant analysis using the multiplied result, calculates a discriminant transformation matrix using the calculated between-class scatter matrix and the calculated within-class scatter matrix, multiplies the discriminant transformation matrix by the block model, outputs the multiplied result as a subbasis matrix, and outputs a set of subbasis matrices generated in all of the blocks included in the e-th layer as the e-th layer basis matrix, and

wherein a number of blocks of each of the layers differs.

2. The apparatus of claim 1, wherein the e-th basis matrix generator includes first through Q-th subbasis matrix generators respectively generating first through Q-th subbasis matrices and outputting a set of the first through Q-th subbasis matrices as the e-th layer basis matrix, Q being a total number of blocks included in the e-th layer, and

wherein the q-th (1≦

q≦

Q) subbasis matrix generator includes;

a block model generator generating the block model using the kernel matrix;

a model transposing unit transposing the block model;

a first multiplier multiplying the zero mean matrix and the transposed block model;

a scatter matrix calculator calculating the between-class scatter matrix and the within-class scatter matrix using the result of multiplied by the first multiplier;

a transformation matrix calculator calculating the discriminant transformation matrix using the between-class scatter matrix and the within-class scatter matrix; and

a second multiplier multiplying the discriminant transformation matrix by the block model and outputting the multiplied result as the q-th subbasis matrix.

3. The apparatus of claim 2, further comprising:

a mean vector calculator calculating a mean vector of the image; and

a subtracting unit subtracting the mean vector from the image and outputting a set of zero mean vectors of the result of subtracting as the zero mean vector.

4. The apparatus of claim 2, wherein the scatter matrix calculator calculates the between-class scatter matrix and the within-class scatter matrix respectively using the following equations:

$S_{\mathrm{gr}}^B=\underset{i=1}{\overset{c}{\sum <br><br>}}<br><br>M_i<br><br>\left(m_{\mathrm{gr}}^i-m_{\mathrm{gr}}\right)<br><br>{\left(m_{\mathrm{gr}}^i-m_{\mathrm{gr}}\right)}^T;<br><br>\mathrm{and}<br><br>\text{\hspace{1em}<br><br>}<br><br>S_{\mathrm{gr}}^W=\underset{i=1}{\overset{c}{\sum <br><br>}}<br><br>\underset{Y_{\mathrm{gr}}\in <br><br>c_i}{\sum <br><br>}<br><br>\left(Y_{\mathrm{gr}}-m_{\mathrm{gr}}^i\right)<br><br>{\left(Y_{\mathrm{gr}}-m_{\mathrm{gr}}^i\right)}^T,$

where S_gr^B is the between-class scatter matrix, S_gr^W is the within-class scatter matrix, M_i is the number of image samples with respect to an i-th class, c is a total number of classes, Y^gr is the result multiplied by the first multiplier, Y_grⁱ is the result multiplied by the first multiplier with respect to the i-th class, m_grⁱ is a mean vector of Y_grⁱ in the i-th class, m_gr is a total mean vector of results multiplied by the first multiplier, T is transpose, c_i is an i-th class, G is a total number of blocks placed on the e-th layer in a horizontal direction, R is a total number of blocks placed on the e-th layer in a vertical direction, 1≦

g≦

G, and 1≦

r≦

R.

5. The apparatus of claim 4, wherein the transformation matrix calculator calculates the discriminant transformation matrix using the following equation:

$W_{\mathrm{gr}}=\underset{W_{\mathrm{gr}}}{\arg <br><br>\text{\hspace{1em}<br><br>}<br><br>\max }<br><br>\frac{<br><br>W_{\mathrm{gr}}^T<br><br>S_{\mathrm{gr}}^B<br><br>W_{\mathrm{gr}}<br><br>}{<br><br>W_{\mathrm{gr}}^T<br><br>S_{\mathrm{gr}}^W<br><br>W_{\mathrm{gr}}<br><br>}.$

6. The apparatus of claim 1, further comprising:

a matrix transposing unit transposing the final basis matrix generated by the first through E-th layer basis matrix generators; and

a feature matrix calculator multiplying the zero mean matrix by the result transposed by the matrix transposing unit and outputting the multiplied result as a feature matrix.

7. The apparatus of claim 6, further comprising:

a storage unit storing the feature matrices outputted from the feature matrix calculator with respect to previous images; and

a correlation calculator calculating a final correlation between the feature matrices outputted from the feature matrix calculator with respect to current images and the feature matrices read from the storage unit with respect to the previous images,

wherein the previous images correspond to the images that have been previously inputted, and the current images correspond to the images that are currently inputted.

8. The apparatus of claim 7, wherein the correlation calculator includes:

first through E-th correlation calculators respectively calculating first through E-th correlations between the current images and the previous images; and

a synthesizing unit synthesizing the first through E-th correlations and outputting the synthesized result as the final correlation,

wherein the e-th correlation calculator calculates the e-th correlation between the previous image and the current image with respect to the e-th layer using the following equation;

$S_e<br><br>\left(a,b\right)=\underset{r=1}{\overset{R}{\sum <br><br>}}<br><br>\underset{g=1}{\overset{G}{\sum <br><br>}}<br><br>W_{\mathrm{gr}}<br><br>\left(\frac{{\left(f_{\mathrm{gr}}^e\right)}^a·<br><br>{\left(f_{\mathrm{gr}}^e\right)}^b}{<br><br>{\left(f_{\mathrm{gr}}^e\right)}^a<br><br>·<br><br><br><br>{\left(f_{\mathrm{gr}}^e\right)}^a<br><br>}\right)$

, where S_e(a,b) is the e-th correlation between the previous image a and the current image b with respect to the e-th layer, W_gr is the discriminant transformation matrix

$\underset{r=1}{\overset{R}{\sum <br><br>}}<br><br>\underset{g=1}{\overset{G}{\sum <br><br>}}<br><br>W_{\mathrm{gr}}=1,$

G is a total number of blocks placed on the e-th layer in a horizontal direction, R is a total number of blocks placed on the e-th layer in a vertical direction, 1≦

g≦

G, 1≦

r≦

R, (f_gr^e)^a is a feature vector of a block placed at a g-th position in a horizontal direction and at a r-th position in a vertical direction on an e-th layer of an image a and the result of multiplying V_gr^T and the zero mean vector, V_gr^T is the result of transposing the result in which the block model is multiplied by the discriminant transformation matrix, (f_gr^e)^b is a feature vector of a block placed at a g-th position in a horizontal direction and at a r-th position in a vertical direction on the e-th layer of an image b, the feature matrix is composed of the first through GR feature vectors, and ∥

∥

is a norm.

9. The apparatus of claim 7, further comprising:

a comparator comparing the final correlation calculated by the correlation calculator with a specified value; and

a correlation determining unit determining a correlation between the previous image and the current image in response to the compared result.

10. A method of processing an image based on layers, the method comprising:

dividing the image into E layers, each layer having at least one block, E being a positive integer equal to or greater than 2; and

generating first through E-th layer basis matrices using the divided image and determining a set of the first through E-th layer basis matrices as a final basis matrix,

wherein the generating of the e-th (1≦

e≦

E) layer basis matrix includes, with respect to each block included in the e-th layer, generating a block model using a kernel matrix obtained by local feature analysis, multiplying a zero mean matrix generated from the divided image by a result of transposing the block model, calculating a between-class scatter matrix and a within-class scatter matrix by linear discriminant analysis using the multiplied result, calculating a discriminant transformation matrix using the calculated between-class scatter matrix and the calculated within-class scatter matrix, multiplying the discriminant transformation matrix by the block model, outputting the multiplied result as a subbasis matrix, and outputting a set of the subbasis matrices generated in all of the blocks included in the e-th layer as an e-th layer basis matrix, and

wherein a number of blocks differs for each of the layers.

11. The method of claim 10, wherein the generating of the e-th basis

matrix comprises generating first through Q-th (where Q is a total number of blocks included in the e-th layer) subbasis matrices and determining a set of the first through Q-th subbasis matrices as the e-th layer basis matrix, and

wherein the generating of the q-th (1≦

q≦

Q) subbasis matrix includes;

generating the block model using the kernel matrix;

transposing the block model;

multiplying the zero mean matrix and the transposed block model;

obtaining the between-class scatter matrix and the within-class scatter matrix using the multiplication result;

obtaining the discriminant transformation matrix using the between-class scatter matrix and the within-class scatter matrix; and

multiplying the discriminant transformation matrix by the block model and determining the multiplied result as the q-th subbasis matrix.

12. The method of claim 10, further comprising:

transposing the final basis matrix; and

multiplying the zero mean matrix by the transposed result and determining the multiplied result as a feature matrix.

13. The method of claim 12, further comprising:

obtaining feature matrices with respect to previous images and storing the obtained feature matrices;

obtaining feature matrices with respect to current images; and

obtaining a final correlation between the feature matrices obtained with respect to the current images and the feature matrices obtained with respect to the stored previous images,

wherein the previous images correspond to the images that have been previously inputted, and the current images correspond to the images that have been currently inputted.

14. The method of claim 13, further comprising:

determining whether the final correlation is equal to or greater than a specified value; and

when the final correlation is at least equal to the specified value, recognizing that the previous images and the current images are similar to one another.

15. An image processing apparatus, the apparatus comprising:

an image divider dividing an the into E layers each having at least one block, E being a positive integer at least equal to 2; and

first through E-th layer basis matrix generators respectively generating first through E-th layer basis matrices based on the divided image and outputting a set of the first through E-th layer basis matrices as a final basis matrix,

wherein an e-th layer basis matrix generator, for each block of an e-th layer, generates a block model using a kernel matrix obtained by local feature analysis, multiplies a zero mean matrix based on the divided image by a result of transposing the block model, calculates a between-class scatter matrix and a within-class scatter matrix by linear discriminant analysis based on the multiplied result, calculates a discriminant transformation matrix based on the between-class scatter matrix and the within-class scatter matrix, multiplies the discriminant transformation matrix by the block model, outputs the multiplied result as a subbasis matrix, and outputs a set of subbasis matrices generated in all of the blocks included in the e-th layer as the e-th layer basis matrix,

wherein e is a positive integer between 1 and E, and

wherein a number of blocks differs for each layer.