Image Matching and Recognition Using Statistical Features After Wavelet Transform

This approach utilizes statistical features derived from wavelet-transformed images for image matching and recognition. The implementation typically involves applying discrete wavelet transform (DWT) algorithms like Haar or Daubechies wavelets through functions such as wavedec2() in MATLAB to decompose images into frequency subbands (approximation, horizontal, vertical, and diagonal details). From these wavelet coefficients, statistical features including mean, variance, energy, and entropy are computed for each subband. These features capture texture characteristics and frequency distribution patterns. The matching process employs similarity measurement algorithms like Euclidean distance or cosine similarity between feature vectors of reference and query images. Key implementation steps include: 1. Preprocessing images to standardize size and format 2. Applying multi-level wavelet decomposition 3. Calculating statistical moments from wavelet coefficients 4. Creating feature vectors for database storage 5. Implementing nearest-neighbor classification with distance metrics Wavelet transformation provides multi-resolution analysis capabilities, enabling extraction of both global and localized features. Combined with statistical characterization, this methodology enhances matching accuracy and robustness by capturing essential image patterns while maintaining invariance to minor distortions and illumination changes.