Visual Relationship Detection with AI

Abstract

Visual Relationship Detection (VRD) enables machines to go beyond object detection and recognize how objects interact within an image. This project combines a pre-trained YOLOv2 deep learning model in MATLAB with heuristic rules to detect object-object relationships, such as “person riding a bicycle” or “dog under table.” Additionally, we evaluate multiple VRD 2 approaches using a Zwicky Box based on technical criteria like accuracy, interpretability, and computational cost. The project is fully documented in a wiki, supported by visualizations.

Introduction

Recognizing object relationships is crucial for scene understanding in autonomous systems, robotics, and surveillance. object detection algorithms such as YOLO only locate objects, but not how they interact. VRD adds this missing layer of contextual understanding.

This project:

Implements a rule-based VRD system in MATLAB using YOLOv2,

Explains each code segment clearly for reproducibility,

Compares different VRD methods using a morphological matrix,

Documents all results in a structured scientific format.

System Overview and Code Explanation

The system is implemented in MATLAB and consists of six main steps. Each is explained below:

Step 1: Load Pre-trained YOLOv2 Network

    detector = yolov2ObjectDetector('tiny-yolov2-coco');

Explanation: This line loads the Tiny YOLOv2 object detector pre-trained on the COCO dataset. COCO includes 80 common object classes such as person, dog, bicycle, etc. This detector will later identify all objects present in the input image.

'tiny-yolov2-coco': Light version of YOLOv2 optimized for speed and low memory.

yolov2ObjectDetector(...): MATLAB's built-in deep learning object detection class.

Step 2: Load Image

 dataDir = fullfile('..', '..', 'Data');
 imageFile = fullfile(dataDir, 'VisualRelationship', 'horseriding.jpg');
 img = imread(imageFile);

Explanation: The input image is loaded from a specified folder from the SVN and displayed to ensure correctness before processing.

Step 3: Run Object Detection

   [bboxes, scores, labels] = detect(detector, img, 'Threshold', 0.5);

   if isempty(bboxes)
   error('No objects detected. Please try a different image or adjust the 
   threshold.');
   end

Explanation: The detect() function returns:

bboxes: Bounding boxes around detected objects,

scores: Confidence levels,

labels: Object class names.

The threshold of 0.5 ensures only detections with >50% confidence are accepted.

Step 4: Infer Visual Relationships

relationships = {};  % to store relationship descriptions

numObjects = size(bboxes, 1); nearThreshold = 100; % pixel threshold for "near" relation

for i = 1:numObjects

   for j = i+1:numObjects
       obj1 = string(labels(i));
       obj2 = string(labels(j));
       box1 = bboxes(i, :);
       box2 = bboxes(j, :);
       
       % Calculate center points of the bounding boxes
       center1 = [box1(1) + box1(3)/2, box1(2) + box1(4)/2];
       center2 = [box2(1) + box2(3)/2, box2(2) + box2(4)/2];
       
       % Calculate differences between centers
       x_diff = center2(1) - center1(1);
       y_diff = center2(2) - center1(2);
       
       relation = ;
       % Rule: Person riding Bicycle (check both orders)
       if contains(lower(obj1), "person") && contains(lower(obj2), "bicycle")
           relation = sprintf('%s riding %s', obj1, obj2);
       elseif contains(lower(obj2), "person") && contains(lower(obj1), "bicycle")
           relation = sprintf('%s riding %s', obj2, obj1);
       % Rule: Person riding Motorcycle
       elseif contains(lower(obj1), "person") && contains(lower(obj2), 
      "motorcycle")
           relation = sprintf('%s riding %s', obj1, obj2);
       elseif contains(lower(obj2), "person") && contains(lower(obj1), 
      "motorcycle")
           relation = sprintf('%s riding %s', obj2, obj1);
       % Rule: Person riding Horse
       elseif contains(lower(obj1), "person") && contains(lower(obj2), "horse")
           relation = sprintf('%s riding %s', obj1, obj2);
       elseif contains(lower(obj2), "person") && contains(lower(obj1), "horse")
           relation = sprintf('%s riding %s', obj2, obj1);
       % Rule: Dog under Table (check vertical positioning)
       elseif contains(lower(obj1), "dog") && contains(lower(obj2), "table") && 
      y_diff > 0
           relation = sprintf('%s under %s', obj1, obj2);
       elseif contains(lower(obj2), "dog") && contains(lower(obj1), "table") && 
        y_diff < 0
           relation = sprintf('%s under %s', obj2, obj1);
       % Rule: Objects near each other (only if they are close enough)
       elseif abs(x_diff) < nearThreshold && abs(y_diff) < nearThreshold
           relation = sprintf('%s near %s', obj1, obj2);
       end
       
       % Add the relation if it's non-empty
       if ~isempty(relation)
           relationships{end+1} = relation;
       end
   end
  end

Explanation: This is the heart of the system. It compares each pair of detected objects and applies hand-coded rules to infer relationships.

“Riding”: Checks for person and bicycle objects close together.

“Under”: Compares vertical positions (y-coordinates).

“Near”: Measures pixel distance between objects and applies a proximity threshold.

This approach is fast, human-readable, and doesn't require additional training data.

Step 5: Visualize Detections

 figure('Name', 'Detections and Relationships', 'NumberTitle', 'off', 'Position', [150 150 1000 450]);

% Left Panel: Display the image with detections subplot(1,2,1); imshow(img); hold on; for i = 1:size(bboxes, 1)

   rectangle('Position', bboxes(i,:), 'EdgeColor', 'r', 'LineWidth', 2);
   % Display the label above the bounding box
   text(bboxes(i,1), bboxes(i,2)-10, string(labels(i)), ...
        'Color', 'yellow', 'FontSize', 12, 'FontWeight', 'bold');

end hold off; title('Detected Objects');

% Right Panel: Prepare the relationship text with extra spacing

% Initialize the annotation text with a header and extra newlines relationshipText = sprintf('Inferred Relationships:\n\n');

if isempty(relationships)

   relationshipText = [relationshipText, 'No significant visual relationships inferred.'];

else

   % Loop through each relationship and add extra space before adding the next
   for i = 1:length(relationships)
       relationshipText = sprintf('%s%s\n\n\n', relationshipText, relationships{i});
   end

end

% Create an annotation textbox on the right side annotation('textbox', [0.55 0.1 0.4 0.8], 'String', relationshipText, ...

          'FontSize', 12, 'Interpreter', 'none', 'EdgeColor', 'none', 'HorizontalAlignment', 'left');

Explanation: Bounding boxes are drawn on the image, and each label (e.g., "person", "dog") is shown near the box. This confirms which objects the network detected before moving to relationship inference.

Step 6: Save the complete Figure as Image

 outputFileName = 'output_detected_with_relationships.png';
 saveas(gcf, outputFileName);
 fprintf('Combined output image saved as %s\n', outputFileName);

Explanation: We save the output showing the image and its relations

Testing & Proof of Functionality

The system was tested with multiple static images (people, objects).

Correct object detection and relationship inference were observed, but not every time.

Visual proofs (annotated outputs) were generated and included in the wiki.

Scientific Documentation

Documentation Format: Structured HSHL Wiki Article.

Figures: Screenshots of detections, relationships, and GUI output.

Animations: GIFs showing object detection and step-by-step relationship reasoning.

Versions: Tracked in wiki revisions.

Conclusion

This project demonstrates a lightweight yet functional AI framework for Visual Relationship Detection using MATLAB. While rule-based heuristics offer simplicity, their accuracy can be improved using data-driven models. However, for real-time and interpretable systems, they remain a strong option. A morphological analysis helped compare different techniques, and the results were scientifically validated and fully documented.

Task

Visual Relationship Detection: This involves identifying the relationships between objects within an image, such as “person riding a bicycle” or “dog under a table”. This task requires not just recognizing the objects but understanding their interactions.

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