Feature extraction algorithm and application examples implemented in Java

With the continuous development of artificial intelligence technology, feature extraction algorithms play an increasingly important role in data processing and pattern recognition. Here, we will introduce a feature extraction algorithm implemented in Java, and demonstrate its use and role through an application example.

1. Introduction to feature extraction algorithm

The feature extraction algorithm refers to processing the original data to extract representative features for subsequent classification, clustering, identification and other operations. . There are various algorithms for extracting features, and commonly used ones include principal component analysis (PCA), linear discriminant analysis (LDA), convolutional neural network (CNN), etc. In this article, we will introduce a feature extraction method based on the LBP algorithm.

LBP algorithm (Local Binary Pattern, local binary pattern) is an algorithm used to describe the local texture features of an image. This algorithm mainly performs binary processing on the surrounding pixels of each pixel in the image. The pixels in the surrounding pixels that are larger than the current pixel are set to 1, otherwise they are set to 0. Finally, these binary results are used to form a binary sequence as the LBP of the pixel. Feature code. Calculate its LBP feature code for each pixel separately, and concatenate all feature codes into a vector, finally obtaining a representative set of lower-dimensional feature vectors.

2. LBP feature extraction algorithm implemented using Java

The following is the core code of the LBP feature extraction algorithm implemented using Java:

public static int[] LBP(Mat src) {
    int rows = src.rows(), cols = src.cols();
    int[] result = new int[rows * cols];
    for (int i = 0; i < rows; i++) {
        for (int j = 0; j < cols; j++) {
            int[] binary = new int[8];
            int center = (int) src.get(i, j)[0];
            binary[0] = (int) src.get(i - 1, j - 1)[0] > center ? 1 : 0;
            binary[1] = (int) src.get(i - 1, j)[0] > center ? 1 : 0;
            binary[2] = (int) src.get(i - 1, j + 1)[0] > center ? 1 : 0;
            binary[3] = (int) src.get(i, j + 1)[0] > center ? 1 : 0;
            binary[4] = (int) src.get(i + 1, j + 1)[0] > center ? 1 : 0;
            binary[5] = (int) src.get(i + 1, j)[0] > center ? 1 : 0;
            binary[6] = (int) src.get(i + 1, j - 1)[0] > center ? 1 : 0;
            binary[7] = (int) src.get(i, j - 1)[0] > center ? 1 : 0;

            int resultValue = 0;
            for (int k = 0; k < binary.length; k++) {
                resultValue += binary[k] * Math.pow(2, k);
            }

            result[i * cols + j] = resultValue;
        }
    }
    return result;
}

This method receives an OpenCV Mat type image as input, then perform LBP feature extraction on it, store the extracted features into a one-dimensional array, and return the array.

3. Application examples

In practical applications, we can use the extracted LBP feature vectors for tasks such as image recognition and face recognition. The following is an application example based on face recognition.

In this application, we first use the LBP feature extraction algorithm implemented in Java to extract features from the training data, and store the extracted feature vectors in the database. Then, when the application receives the face image to be recognized, it also uses the feature extraction algorithm to extract its feature vector and compares it with the feature vector in the database to determine whether the person is a known person.

The following is a sample code for face recognition application based on Java:

public String recognition(Mat src) {
    int[] feature = LBP(src);

    String result = "Unknown";

    double minDist = Double.MAX_VALUE;
    for (int[] f : features) {
        double distValue = getDist(feature, f);
        if (distValue < minDist) {
            minDist = distValue;
            result = "Person-" + String.valueOf(features.indexOf(f) + 1);
        }
    }

    return result;
}

This method receives the Mat type image of OpenCV, and then calls the LBP feature extraction algorithm to obtain the feature vector of the image. Then, the method compares the feature vector with the feature vectors previously stored in the database, and finally returns a face recognition result.

4. Summary

This article introduces a feature extraction algorithm implemented in Java and its application examples. The algorithm is simple to use, highly efficient, and can be applied to a variety of application scenarios. I hope that readers can better understand and apply feature extraction algorithms to improve the efficiency of data analysis and pattern recognition through the introduction of this article.

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