MATLAB Source Code Implementation for GIST Feature Extraction

GIST feature is a widely used global feature descriptor in the field of image processing, particularly suitable for scene classification and object recognition. Its core concept simulates the human visual system's perception of scenes by converting images into low-dimensional feature vectors.

For beginners, implementing GIST feature extraction in MATLAB is straightforward. The typical implementation involves the following key steps with corresponding code considerations:

Image Preprocessing: First convert the input image to grayscale using MATLAB's rgb2gray() function, and perform necessary size normalization with imresize() to ensure effectiveness of subsequent steps. This typically involves resizing images to a standard dimension like 256x256 pixels.

Multi-scale Filtering: Apply Gabor filter banks (usually containing multiple orientations and scales) for image filtering. In MATLAB, this can be implemented using gabor() function to create filter banks and imfilter() for convolution operations, capturing texture features at different orientations and frequencies.

Block-wise Statistics: Divide the filtered image into multiple blocks (such as 4x4 or other configurations) using blockproc() function. Calculate the mean or other statistical measures of filter responses within each block using mean2() to form local features.

Feature Concatenation: Concatenate statistical results from all blocks into a long vector using horzcat() or cell2mat() functions, forming the final GIST feature descriptor. The resulting vector typically has dimensions like 512 elements for a 4x4 block configuration with 8 orientations.

Dimensionality Reduction (Optional): For certain applications, use PCA with pca() function or other dimensionality reduction techniques to reduce feature dimensions, decreasing computational load and improving generalization capability.

In MATLAB, the Image Processing Toolbox provides essential functions for implementing these steps, particularly for Gabor filter construction and convolution operations. Beginners are advised to start with simple image datasets (such as indoor/outdoor scene classification datasets) and gradually understand GIST feature characteristics and their performance in different tasks through experimental validation.

It's important to note that GIST feature performance highly depends on parameter settings, including the number of filter orientations, scales, and block sizes. Therefore, parameter tuning using optimization techniques like grid search may be necessary for specific applications to achieve optimal results.