how to reduce feature length of local binary pattern for content based image retrieval?

 i need matlab code for this please

Answer 1

[Akanksha]

Hey Himanshu Tyagi ,

To reduce the feature length of Local Binary Pattern (LBP) for content-based image retrieval, you can use uniform patterns. Here's a simple implementation:

function lbp_features = uniform_lbp(image, radius, neighbors)
% Convert image to grayscale if it's not already
if size(image, 3) == 3
    image = rgb2gray(image);
end
image = double(image);

% Initialize output
lbp = zeros(size(image));

% Calculate weights
weights = 2.^(0:neighbors-1);

% Generate circular neighbor coordinates
[y, x] = meshgrid(-radius:radius, -radius:radius);
angle = atan2(y, x);
r = sqrt(x.^2 + y.^2);
circle_mask = (r <= radius+0.5) & (r >= radius-0.5);
angles = linspace(0, 2*pi, neighbors+1);
angles = angles(1:end-1);

% Compute LBP
for i = 1:neighbors
    y_offset = round(-radius * sin(angles(i)));
    x_offset = round(radius * cos(angles(i)));
    neighbor = circshift(image, [y_offset, x_offset]);
    lbp = lbp + (neighbor > image) .* weights(i);
end

% Uniform pattern mapping
uniform_map = zeros(256, 1);
uniform_patterns = 0;
for i = 0:255
    pattern = bitget(i, 1:8);
    transitions = sum(abs(diff([pattern(8), pattern, pattern(1)])));
    if transitions <= 2
        uniform_map(i+1) = uniform_patterns;
        uniform_patterns = uniform_patterns + 1;
    else
        uniform_map(i+1) = neighbors + 1;
    end
end

% Apply uniform mapping
lbp = uniform_map(lbp + 1);

% Compute histogram
lbp_features = histcounts(lbp, 0:(neighbors+2));

% Normalize histogram
lbp_features = lbp_features / sum(lbp_features);
end

To use the above function use the following code :

% Load an image (replace with your image filename)
image = imread('input_image.jpg');

% Extract LBP features
radius = 1;
neighbors = 8;
lbp_features = uniform_lbp(image, radius, neighbors);

% Display the features
figure;
bar(lbp_features);
xlabel('LBP Uniform Pattern');
ylabel('Normalized Frequency');
title('Uniform LBP Histogram');

Following is the output generated when the functions are invoked with different inputs.

Please refer to the following related documentation that to help you further :

https://www.mathworks.com/help/vision/ug/image-retrieval-with-bag-of-visual-words.html

https://www.mathworks.com/help/vision/ref/extractlbpfeatures.html

https://www.mathworks.com/help/images/product-description.html

Hope this helps. Thanks.