Image pattern recognition system and method

An image pattern recognition system for detecting a pattern from at least one image based on detected interest points in the at least one image, the pattern recognition system comprising:

• one or more processors;
• stored instructions that, when executed, cause the one or more processors to perform operations including:
• extracting feature vectors, each feature vector comprising data derived from video content in a region in a predetermined relation to a location of a respective detected interest point in the at least one image;
• computing a set of bin values for bins associated with leaf nodes of decision trees of a forest of decision trees, the bin value being computed for each bin by summing contributions corresponding to the feature vectors, each contribution being computed dependent on selections of feature vector components and thresholds associated with non-leaf nodes along a path through the decision tree to the leaf node associated with the bin;
• computing a pattern detection result by comparing the set of bin values to reference sets of values,
• wherein at least a first non-leaf node from the non-leaf nodes of the decision trees has an associated first scale value and at least a second non-leaf node from the non-leaf nodes of the decision trees has an associated second scale value, the computing of the set of bin values using a soft decision function in the computation of the contributions for the bins associated with leaf nodes at ends of paths through the decision tree that include the first non leaf node, the contributions being computed in proportion to a result of applying the soft decision function to a ratio of the first scale value and a difference between the threshold and a feature value taken from the feature vector according to the selection of the feature vector component associated with the first non-leaf node, the second scale value being used in the computation of the contributions for the bins associated with leaf nodes at ends of paths through the decision tree that include the second non leaf node, the operations performed by the one or more processors further including:
• computing the first and second scale value by determining a first and second measure of statistical spread of the feature values taken from the feature vectors of a training set according to the selection of the feature vector component associated with the first and second non-leaf node respectively, and setting the first and second scale value to a product of a common factor and the first and second measure of statistical spread respectively.