Method and apparatus for object recognition

An apparatus for recognizing an object, the apparatus comprising:

• means for local image sampling which samples a plurality of local regions of an image of the object by stepping a local window in discrete steps over the image to generate a set of sample vectors;
• means for dimensionality reduction which trains a self-organizing map on the set of sample vectors, and steps, in discrete steps, the local window over any images contained in any training and any test sets, thereby producing a resultant local image sample at each one of the discrete steps, and generates a new training set in an output space of the self-organizing map by passing the resulting local image samples through the self-organizing map;
• means for feature extraction which extracts features of the object from the sample vectors and trains a convolutional neural network on the newly generated training set;
• means for classification which classifies the object into a known classification using the extracted features.

An apparatus for recognizing an object, the apparatus comprising:

• means for local image sampling which samples a plurality of local regions of an image of the object by stepping a local window in discrete steps over the image to generate a set of sample vectors;
• means for dimensionality reduction wherein the set of sample vectors is mapped into a space of lower dimension wherein similar points in the input space are mapped to similar points in the output space;
• means for feature extraction which extracts features from the sample vectors;
• means for classification which estimates a probability that the image contains an individual member of a group based on the extracted features;
• means for confidence estimation which estimates a confidence that the individual member of the group is contained within the image;
• wherein the feature extraction means successively transforms the output of the local image sampling means into a plurality of new representations which successively correspond to a set of higher level features of the image;
• and
• wherein the confidence estimation means generates the confidence according to y.sub.m (y.sub.m -y.sub.2m), where y.sub.m is a first maximum output and y.sub.2m is a second maximum output of the classification means.