Sign language method using clustering

1. A computer-implemented method of recognizing sign language, the method comprising:

detecting, via circuitry, one or more interest points in an extracted sign language feature, wherein the one or more interest points are localized in space and time in each of a plurality of images acquired from a plurality of frames of a sign language video including the extracted sign language feature, wherein the detecting is carried out using a Scale Invariant Features Transform (SIFT) descriptor and the interest points represent corners in each image;

applying a digital filter to determine one or more extreme of a central region of the one or more interest points;

associating one or more features with each interest point of the one or more interest points using a neighboring pixel function;

clustering, via the circuitry, a group of extracted sign language features from the plurality of images based on a similarity between the extracted sign language features according to the associating to form from 800 to 1,200 clusters;

representing each image of the plurality of images by a histogram of visual words corresponding to the respective image to generate a code book;

training, via the circuitry, a classifier based on labels assigned to the plurality of images to classify each extracted sign language feature using the code book;

detecting, via the circuitry, a posture in each frame of the plurality of frames of the sign language video using the trained classifier;

constructing, via the circuitry, a sign gesture based on the detected postures, and

identifying text words that correspond with the sign gesture and presenting the text on a display so as to ease communication between deaf people and non-deaf people.

2. The method of claim 1 , wherein the number of clusters is about 900.

3. The method of claim 1 , wherein the digital filter includes a Harris Laplace.

4. The method of claim 1 , further comprising:

training a Support Vector Machine to classify each frame of the sign language video.

5. The method of claim 1 , further comprising:

classifying each sign gesture using a K-Nearest Neighbors (K-NN) classifier.

6. The method of claim 1 , further comprising:

splitting a sequence of postures, and generating a split histogram of the postures for the respective split sequence of postures; and

concatenating the split sequence of postures to preserve an order of the sequence of postures in the respective sign gesture.

7. The method of claim 6 , further comprising:

normalizing the split histogram of the sequence of postures of the sign language video to account for a difference in signing speed.