Method and apparatus for image texture describing

A method for retrieving an image texture descriptor for describing texture features of an image, comprising the steps of:

• (a) filtering input images using predetermined filters having different scale coefficients;
• (b) projecting the filtered images onto axes of each predetermined direction to obtain data groups consisting of averages of each directional pixel values;
• (c) determining a plurality of indicators based on scale coefficients of the filters used in filtering data groups selected among the data groups by a predetermined selection method;
• (d) determining the plurality of indicators as the texture descriptor of the image.

A method for retrieving an image texture descriptor for describing texture features of an image, comprising the steps of:

• (a) filtering input images using predetermined filters having different orientation coefficients and different scale coefficients;
• (b) projecting the filtered images onto axes of each predetermined direction to obtain graphs consisting of averages of each directional pixel values;
• (c) selecting candidate graphs among the graphs obtained in the step (b) by a predetermined classification method;
• (d) determining another indicator based on the presence of graphs filtered by filters having scale coefficients or orientation coefficients which are close to or identical with the scale coefficients or orientation coefficients of the filters used in filtering the selected candidate graphs;
• (e) determining a plurality of indicators based on scale coefficients or orientation coefficients of the filters used in filtering the determined candidate graphs;
• and
• (f) determining the indicator determined in the step (d) and the plurality of indicators determined in the step (e) as the texture descriptor of the image.

A method for retrieving an image texture descriptor for describing texture features of an image, comprising the steps of:

• (a) filtering input images using predetermined filters having different orientation coefficients and different scale coefficients;
• (b) projecting the filtered images onto horizontal and vertical axes to obtain horizontal-axis projection graphs and vertical-axis projection graphs;
• (c) calculating normalized auto-correlation values for each graph;
• (d) obtaining local maximums and local minimum for each normalized auto-correlation value, at which the calculated normalized auto-correlation values forms a local peak and a local valley at a predetermined section;
• (e) defining the average of the local maximums and the average the local minimums as contrast;
• (f) selecting graphs in which the ratio of the standard deviation to the average of the local maximums is less than or equal to a predetermined threshold as first candidate graphs;
• (g) determining the type of the second candidate graphs according to the number of graphs filtered by the filters having scale coefficients or orientation coefficients which are close to or identical with the scale coefficients or orientation coefficients of the filters used in filtering the selected second candidate graphs;
• (h) counting the numbers of graphs belonging to the respective types of second candidate graphs and determining predetermined weights of each type of second candidate graphs;
• (i) calculating the sum of products of the counted numbers of graphs and the determined weights to determine the calculation result value as a first indicator constituting a texture descriptor;
• (j) determining the orientation coefficients and scale coefficients of the second candidate graphs having the biggest contrast as second through fifth indicators;
• and
• (k) determining indicators including the first indicator and the second through fifth indicators as the texture descriptors of the corresponding image.

A computer readable medium having program codes executable by a computer to perform a method for an image texture descriptor for describing texture features of an image, the method comprising the steps of:

• (a) filtering input images using predetermined filters having different orientation coefficients and different scale coefficients;
• (b) projecting the filtered images onto horizontal and vertical axes to obtain horizontal-axis projection graphs and vertical-axis projection graphs;
• (c) calculating normalized auto-correlation values for each graph;
• (d) obtaining local maximums and local minimums for each of normalized auto-correlation values, at which the calculated normalized auto-correlation value forms a local peak and a local valley at a predetermined section;
• (e) defining the average of the local maximums and the average the local minimums as contrast;
• (f) selecting graphs in which the ratio of the standard deviation to the average of the local maximums is less than or equal to a predetermined threshold as first candidate graphs;
• (g) determining the type of the second candidate graphs according to the number of graphs filtered by the filters having scale coefficients or orientation coefficients which are close to or identical with the scale coefficients or orientation coefficients of the filters used in filtering the selected second candidate graphs;
• (h) counting the numbers of graphs belonging to the respective types of second candidate graphs and determining predetermined weights of each type of second candidate graph;
• (i) calculating the sum of products of the counted numbers of graphs and the determined weights to determine the calculation result value as a first indicator constituting a texture descriptor;
• (j) determining the orientation coefficients and scale coefficients of the second candidate graphs having the biggest contrast as second through fifth indicators;
• and
• (k) determining indicators including the first indicator and the second through fifth indicators as the texture descriptors of the corresponding image.

An apparatus method for retrieving an image texture descriptor for describing texture features of an image, comprising:

• filtering mean for filtering input images using predetermined filters having different orientation coefficients;
• projecting means for projecting the filtered images onto axes of each predetermined direction to obtain data groups consisting of averages of each directional pixel values;
• classifying means for selecting candidate data groups among the data groups by a predetermined classification method;
• first indicator determining means for determining another indicator based on the number of graphs filtered by filters having scale coefficients or orientation coefficients which are close to or identical with the scale coefficients or orientation coefficients of the filters used in filtering the selected candidate graph;
• and
• second indicator determining means for determining a plurality of indicators based on scale coefficients and orientation coefficients of the filters used in filtering the determined candidate graphs.

An apparatus for retrieving an image texture descriptor for describing texture features of an image, comprising:

• a filtering unit for filtering input images using predetermined filters having different orientation coefficients and different scale coefficients;
• an image mean/variance calculating unit for calculating the mean and variance of pixels with respect to each of the filtered images, and obtaining a predetermined vector using the calculated mean and variance;
• a projecting unit for projecting the filtered images onto horizontal and vertical axes to obtain horizontal-axis projection graphs and vertical-axis projection graphs;
• a calculating unit for calculating a normalized auto-correlation value for each graph;
• a peak detecting/analyzing unit for detecting local maximums and local minimums for each auto-correlation value, at which the calculated normalized auto-correlation values forms a local peak and a local valley at a predetermined section;
• a mean/variance calculating unit for calculating the average of the local maximums and the average the local minimums;
• a first candidate graph selecting/storing unit for selecting the graphs satisfying the requirement that the ratio of the standard deviation to the average of the local maximums be less than or equal to a predetermined threshold, as first candidate graphs;
• a second candidate graph selecting/storing unit for applying a predetermined clustering algorithm to the first candidate graphs to select the same as second candidate graphs;
• a classifying unit for counting the number of graphs belonging to each of the respective types of the second candidate graphs, outputting data signals indicative of the number of graphs of each type, determining predetermined weights of the graphs belonging to the respective types and outputting data signals indicative of weights to be applied to each type;
• a first indicator determining unit for calculating the sum of the products of the data representing the number of graphs belonging to each type, and the data representing the weights to be applied to each type, determining and outputting the calculation result as a first indicator constituting a texture descriptor;
• a contrast calculating unit for calculating the contrast according to formula (2) using the averages output from the mean/variance calculating unit and outputting a signal indicating that the calculated contrast is biggest;
• a second candidate graph selecting/storing unit for outputting the candidate graphs having the biggest contrast among the second candidate graphs stored therein in response to the signal indicating that the calculated contrast is biggest;
• a second-to-fifth indicator determining unit for determining the orientation coefficient of a graph having the biggest contrast, among the horizontal-axis projection graphs;
• the orientation coefficient of a graph having the biggest contrast, among the vertical-axis projection graphs, as a second indicator;
• the scale coefficient of a graph having the biggest contrast, among the horizontal-axis projection graphs, as a fourth indicator;
• and the scale coefficient of a graph having the biggest contrast, among the vertical-axis projection graphs, as a fifth indicator;
• and
• a texture descriptor output unit for combining the first indicator, the second through fifth indicators and the predetermined vector and outputting the combination result as the texture descriptors of the corresponding image.