Image Clustering and Classification

How Do Machines Recognize Your Pet?

Exploring Machine Learning in Distinguishing Cats and Dogs.

Try This:

(1) Begin by powering on your iPhone and navigate to your photo album.

(2) Search through your collection to find a favorite picture of your beloved cat or dog.

(3) Once you’ve selected an image, swipe upwards on the screen.

Do you notice how your iPhone offers insights about the breed of your cherished pet?

Apple incorporates an extensive database filled with countless images to train its machines. This technology allows your iPhone to recognize various pet breeds by comparing your pet’s image with the ones in its database.

Introduction and Database Utilization

To explore how the Orange software processes and categorizes visual data, I conducted an experiment using a labeled image dataset of cats and dogs from Kaggle. Following a structured data analysis workflow within Orange, I imported the images and extracted their visual features. The software then arranged the images into a visual grid, allowing for an interpretable examination of the feature-based clustering and the underlying patterns used by the algorithm to differentiate between categories.

Clustering Techniques

Grid Representation

By consolidating the cat and dog images into a single directory and importing them into Orange, the software automatically applies the Inception v3 convolutional neural network to extract high-level visual features from each image. These features—representing complex patterns such as texture, shape, and composition—are then used as input for unsupervised clustering based on Orange’s default parameters. The resulting clusters are visualized in a grid format, allowing for intuitive exploration of how the model groups similar images. This workflow demonstrates how pre-trained deep learning models can be leveraged within Orange to facilitate automated image categorization without the need for manual annotation.

Interestingly, Orange effectively differentiates between cats and dogs. Observing the grid, it’s noticeable that cats predominantly occupy the left section, while dogs are grouped on the right.

Hierarchical Clustering

To delve deeper into how Orange classifies these images, I utilized its “hierarchical clustering” feature.

This function helps visualize the relationships and distinctions between grouped photos.

Controlled Experiment

I organized the same image set as follows into predefined folders by species for a controlled test. This setup allows Orange to independently scan and analyze each folder, comparing its findings with the following pre-assigned classifications test.

Analyzing the first-layer classification (species)...

The initial layer of classification interestingly does not separate cats from dogs but instead prioritizes images based on the presence of light golden fur. As a result, the selected images predominantly feature light golden-haired cats. This dataset notably lacks dogs with similar fur coloration, explaining their absence.

Test Results

The confusion matrix and testing scores reveal that Orange performs exceptionally well in distinguishing between cats and dogs. It boasts a 100% accuracy rate, matching the classifications indicated by the folder labels. This experiment highlights the effectiveness of machine learning in recognizing and differentiating between animal species based on visual data.

Further into the clusters (breeds)...

As clustering deepens within the dog category, Orange demonstrates strong facial recognition capabilities, grouping dogs with similar facial features. This suggests the model, likely powered by deep neural networks like Inception v3, can detect and analyze subtle visual patterns such as snout shape and eye spacing with high precision.

However...

The machine is not doing well in classifying breeds because it considers color more than breeds.

Predictions on Machine Learning Abilities based on the test

While the model effectively distinguishes between broad animal species such as cats and dogs, its poor performance in breed classification highlights limitations in feature representation and discrimination. Specifically, the model’s overreliance on superficial features like color, rather than more intrinsic morphological traits, indicates that it lacks a nuanced understanding of fine-grained differences. This suggests several key points:

Feature Representation Challenge: The model tends to prioritize easily detectable features (e.g., color), which may not be discriminative for subtle class differences like breeds, leading to suboptimal fine-grained recognition.
Data and Annotation Quality: Accurate breed classification requires diverse, well-labeled datasets that capture the variability within and across breeds to help the model generalize beyond trivial cues.
Model Architecture and Training Strategies: Enhancing model depth, incorporating attention mechanisms, or applying multi-task learning could improve the model’s ability to capture complex, fine-grained patterns necessary for distinguishing similar categories.

In summary, improving fine-grained classification performance demands advances not only in data quality but also in feature learning and model design, enabling the machine to grasp more intrinsic, discriminative characteristics beyond superficial appearance.

Page updated

Report abuse