Content based image retrieval

The method according to claim 1, wherein the color feature descriptor and texture feature descriptor are further combined with at least a shape feature descriptor and an orientation feature descriptor.

The method according to claim 1, wherein the determined color feature descriptor and texture feature descriptor may comprise only global statistical measurements of at least the mean and variance of the numerical values for each of three color channels of each one of at least three different color spaces.

The method according to claim 1, further comprising, computing a local region of interest, or ROI, of a certain ROI size within the query image in order to distinguish different color and/or texture regions in the query image, wherein the ROI is computed by measuring mean and variance, where the ROI satisfies a distance nearest or farthest to the global statistical measurements.

The method according to claim 4, wherein the ROI size is a percentage of a total area of the query image.

The method according to claim 1, wherein the different color spaces combined simultaneously comprise at least HSV, CieLAB and CieXYZ.

The method according to claim 2, wherein the shape feature descriptor comprises using low-level invariant Hu moments and other non-linear and dimensionless parameters related to a volume of an object, based on areas comprising convex hulls, eccentricities, other derivate computed shapes, and/or ratios thereof related to the object.

The method according to claim 7, further comprising computing:

• equivalent perimeter-based ratios and detailed local shapes for inside contours and other high-level variables;
• and/or
• convexity defect areas and related linear ratios for inside and outside borders, convexities and/or eccentricities.

The method according to claim 2, wherein the orientation feature descriptor is determined using at least one angle value of the first component of a principal component analysis, or PCA.

The method according to claim 10, wherein the m-dimensional radius vector is computed for each dimension using at least the query image position, changing mathematically and geometrically from a multidimensional space to a one-dimensional space.

The method according to claim 1, wherein the at least two feature descriptors are optimized by applying transform techniques and filtering methods based on statistical classification methods applied to a multi-variant logistical regression analysis.

The non-transitory computer readable medium according to claim 13, wherein the color feature descriptor and texture feature descriptor are further combined with at least a shape feature descriptor and an orientation feature descriptor.

The non-transitory computer readable medium according to claim 13, wherein the determined color feature descriptor and texture feature descriptor may comprise only global statistical measurements of at least the mean and variance of the numerical values for each of three color channels of each one of at least three different color spaces.

The non-transitory computer readable medium according to claim 13, further comprising computing a local region of interest, or ROI, of a certain ROI size within the query image in order to distinguish different color and/or texture regions in the query image, wherein the ROI is computed by measuring mean and variance, where the ROI satisfies a distance nearest or farthest to the global statistical measurements.

The non-transitory computer readable medium according to claim 16, wherein the ROI size is a percentage of a total area of the query image.

The non-transitory computer readable medium according to claim 13, wherein the different color spaces combined simultaneously comprise at least HSV, CieLAB, and CieXYZ.

The non-transitory computer readable medium according to claim 14, wherein the shape feature descriptor comprises using low-level invariant Hu moments and other non-linear and dimensionless parameters related to a volume of object, based on areas comprising convex hulls, eccentricities, other derivate computed shapes, and/or ratios thereof related to the object.

The non-transitory computer readable medium according to claim 19, further comprising computing:

• equivalent perimeter-based ratios and detailed local shapes for inside contours and other high-level variables;
• and/or
• convexity defect areas and related linear ratios for inside and outside borders, convexities, and/or eccentricities.

The non-transitory computer readable medium according to claim 14, wherein the orientation feature descriptor is determined using at least one angle value of a first component of a principal component analysis, or PCA.

The non-transitory computer readable medium according to claim 22, wherein the m-dimensional radius vector is computed for each dimension using at least the query image position, changing mathematically and geometrically from a multidimensional space to a one-dimensional space.

The non-transitory computer readable medium according to claim 13, wherein the at least two feature descriptors are optimized by applying transform techniques and filtering methods based on statistical classification methods applied to a multi-variant logistical regression analysis.