Method and apparatus for object recognition

Claim 1

A method for recognizing an object having an object having a set of features, the method comprising the steps of: sampling 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; reducing the dimensionality of the sample vectors, including (a) training a self-organizing map on the set of sample vectors, (b) stepping, 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 (c) generating 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; extracting the features of the object from the dimensionally reduced sample vectors, including training a convolutional neural network on the newly generated training set; and classifying the object into a known classification using the extracted features.

Claim 2

A method for recognizing an object having an object having a set of features, the method comprising the steps of: sampling 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; reducing the dimensionality of the sample vectors, including (a) training a self-organizing map on the set of sample vectors, (b) stepping, 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 (c) generating 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; extracting the features of the object from the dimensionally reduced sample vectors, including training a convolutional neural network on the newly generated training set; and classifying the object into a known classification using the extracted features.

Claim 4

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.

Claim 5

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.

Claim 12

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 m (y m -y 2m ), where y m is a first maximum output and y 2m is a second maximum output of the classification means.

Claim 13

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 m (y m -y 2m ), where y m is a first maximum output and y 2m is a second maximum output of the classification means.