Field of the Invention
The disclosure relates to a signal processing method and signal processing apparatus and, more particularly, to a technique for extracting a feature amount suited to pattern identification from multi-dimensional data such as image data.
Description of the Related Art
As a feature amount effective for pattern identification such as face recognition, an LGBP (Local Gabor Binary Pattern) described in W. Zhang, S. Shan, W. Gao, X. Chen, H. Zhang, “Local Gabor Binary Pattern Histogram Sequence (LGBPHS): A Novel Non-Statistical Model for Face Representation and Recognition”, Proc. IEEE International Conference on Computer Vision, pp. 768-791, 2005. has been proposed. The LGBP is a feature amount obtained by applying several ten Gabor Wavelet filters to input image data, and extracting, from image data after the filtering, an LBP (Local Binary Pattern) described in T. Ojala, M. Pietikainen, D. Harwood, “A Comparative Study of Texture Measures with Classification Based on Featured Distributions”, Pattern Recognition, Vol. 29, 51-59, 1996.
FIG. 14 shows overview of processing required to extract an LBP. The LBP is an 8-bit feature amount calculated based on a pixel of interest (x, y) and eight pixels (to be referred to as reference pixels (x+xn, y+yn) hereinafter) which neighbor the pixel of interest, as given by:
                              LBP          ⁡                      (                          x              ,              y                        )                          =                              ∑                          n              =              0                        7                    ⁢                                          ⁢                                    s              ⁡                              (                                                      i                    ⁡                                          (                                                                        x                          +                                                      x                            n                                                                          ,                                                  y                          +                                                      y                            n                                                                                              )                                                        -                                      i                    ⁡                                          (                                              x                        ,                        y                                            )                                                                      )                                      ·                          2              n                                                          (        1        )            where i(x, y): an input pixel value at coordinates (x, y),
LBP (x, y): an LBP at the coordinates (x, y), and
(xn, yn): relative positions of the reference pixels with respect to the pixel of interest,
and
                              s          ⁡                      (            u            )                          =                  {                                                    1                                                                                  if                    ⁢                                                                                  ⁢                    u                                    ≥                  th                                                                                    0                                                                                  if                    ⁢                                                                                  ⁢                    u                                    <                  th                                                                                        (        2        )            
Note that in the example of FIG. 14, a threshold th=0. Also, assuming that a pixel immediately above the pixel of interest is set to be (x0, y0)=(0, 1), the relative positions (xn, yn) are set to be (x1, y1)=(1, 1), (x2, y2)=(1, 0), . . . , (x7, y7)=(−1, 1) so as to surround the pixel of interest clockwise.
As described above, the LGBP is popularly used in face recognition and the like as a feature amount effective for pattern identification, but several ten Gabor Wavelet filters are required to be used so as to obtain high identification accuracy. For example, when 40 Gabor Wavelet filters are used, a data amount of the LGBP generated as a result of the processing is “output data size×40×8 bits”. For example, upon calculating the data amount to have an output data size=64×64, the data amount of the LGBP is about 1.3 Mbits. That is, in case of the LGBP, especially when pattern identification is implemented as hardware, a cost of the overall apparatus increases.