Apparatus and method for pattern recognition

A pattern recognition apparatus comprising:

• a pattern input, unit configured to input a pattern of a recognition object;
• a feature extraction unit configured to perform feature extraction from the input pattern to generate an input feature vector;
• a dictionary feature storing unit configured to store a quantized dictionary feature vector of the recognition object;
• a similarity calculation unit configured to calculate a similarity between the input feature vector and the quantized dictionary feature vector;
• and
• a determination unit configured to recognize the recognition object based on the similarity, wherein the dictionary feature storing unit includes:
• a dictionary input unit configured to input a dictionary pattern of the recognition object;
• a feature extraction unit configured to perform feature extraction from the input pattern to generate a feature vector;
• a function generation unit configured to calculate a quantization threshold of each of the quantization number, the function generation unit calculating the quantization threshold of quantization number (n+1) using a quantization threshold of quantization number n (n>=1) and generating a quantization function having a quantization threshold corresponding to quantization number S (S>n);
• a quantization unit configured to quantize each component of the feature vector of the dictionary pattern using the quantization function to generate an dictionary quantization feature vector having each of the quantized component;
• and
• a dictionary feature storing unit configured to store the quantized dictionary feature vector.

A pattern recognition method comprising steps of:

• inputting a pattern of a recognition object;
• performing feature extraction from the input pattern to generate a feature vector;
• increasing the number of quantization in an order from quantization number 1 or quantization number 2 to calculate a quantization threshold of each of the quantization number, wherein the quantization threshold of quantization number (n+1) using a quantization threshold of quantization number n (n>=1) is calculated and a quantization function having a quantization threshold corresponding to quantization number S (S>n) is generated;
• quantizing each component of the feature vector of the input pattern using the quantization function to generate an input quantization feature vector having each of the quantized component;
• storing a dictionary feature vector of the recognition object, or a quantized dictionary feature vector in which each component of the dictionary feature vector of the pattern of a recognition object is quantized;
• calculating a similarity between the input quantization feature vector and the dictionary feature vector, or a similarity between the input quantization feature vector and the quantized dictionary feature vector;
• and
• recognizing the recognition object based on the similarity.

A pattern recognition method comprising steps of:

• inputting a pattern of a recognition object;
• performing feature extraction from the input pattern to generate an input feature vector;
• storing a quantized dictionary feature vector of the recognition object;
• calculating a similarity between the input feature vector and the quantized dictionary feature vector;
• and
• recognizing the recognition object based on the similarity,
• wherein the storing step includes:
• inputting a dictionary pattern of the recognition object;
• performing feature extraction from the input pattern to generate a feature vector;
• calculating a quantization threshold of each of the quantization number, wherein the quantization threshold of quantization number (n+1) using a quantization threshold of quantization number n (n>=1) is calculated and a quantization function having a quantization threshold corresponding to quantization number S (S>n) is generated;
• quantizing each component of the feature vector of the dictionary pattern using the quantization function to generate an dictionary quantization feature vector having each of the quantized component;
• and
• storing the quantized dictionary feature vector.

A non-transitory computer-readable medium storing a pattern recognition program configured to perform steps of:

• inputting a pattern of a recognition object;
• performing feature extraction from the input pattern to generate a feature vector;
• increasing the number of quantization in an order from quantization number 1 or quantization number 2 to calculate a quantization threshold of each of the quantization number, wherein the quantization threshold of quantization number (n+1) using a quantization threshold of quantization number n (n>=1) is calculated and a quantization function having a quantization threshold corresponding to quantization number S (S>n) is generated;
• quantizing each component of the feature vector of the input pattern using the quantization function to generate an input quantization feature vector having each of the quantized component;
• storing a dictionary feature vector of the recognition object, or a quantized dictionary feature vector in which each component of the dictionary feature vector of the pattern of a recognition object is quantized;
• calculating a similarity between the input quantization feature vector and the dictionary feature vector, or a similarity between the input quantization feature vector and the quantized dictionary feature vector;
• and
• recognizing the recognition object based on the similarity.

A non-transitory computer-readable medium storing a pattern recognition program configured to perform steps of:

• inputting a pattern of a recognition object;
• performing feature extraction from the input pattern to generate an input feature vector;
• storing a quantized dictionary feature vector of the recognition object;
• calculating a similarity between the input feature vector and the quantized dictionary feature vector;
• and
• recognizing the recognition object based on the similarity;
• wherein the storing step includes:
• inputting a dictionary pattern of the recognition object;
• performing feature extraction from the input pattern to generate a feature vector;
• calculating a quantization threshold of each of the quantization number, wherein the quantization threshold of quantization number (n+1) using a quantization threshold of quantization number n (n>=1) is calculated and a quantization function having a quantization threshold corresponding to quantization number S (S>n) is generated;
• quantizing each component of the feature vector of the dictionary pattern using the quantization function to generate an dictionary quantization feature vector having each of the quantized component;
• and
• storing the quantized dictionary feature vector.