AI-Powered Product Color Variation Management: Enhancing Consumer Experience Through Machine Learning

Author: Sasibhushan Rao Chanthati
Address: 11120 Hollowbrook Rd, Owings Mills, MD, 21117, USA
Published In: Engineering and Technology Journal, Volume 09, Issue 11, November 2024, Pages 5513-5516. DOI: 10.47191/etj/v9i11.08

Introduction: The Role of AI in Product Aesthetics

Color significantly influences consumer purchasing decisions, and businesses continuously strive to optimize product aesthetics to attract customers. In an era where artificial intelligence (AI) and machine learning (ML) are reshaping industries, integrating these technologies into product design and marketing can provide a competitive edge.

The paper “Second Version on the Product Color Variation Management using Artificial Intelligence” by Sasibhushan Rao Chanthati explores how AI and deep learning models can dynamically adjust product colors to align with consumer preferences, thereby enhancing engagement and boosting sales.

This study presents an AI-driven workflow that analyzes images uploaded by users, detects color variations, and leverages machine learning models to classify and optimize color presentation.

Key Objectives of AI in Color Variation Management

1. Automated Color Analysis: Detect modifications in product images based on color saturation and brightness levels.
2. Machine Learning Integration: Use convolutional neural networks (CNNs) to classify images as “Modified” or “Original.”
3. Consumer-Centered Customization: Adapt product colors in real time to enhance customer appeal.
4. E-Commerce Transparency: Ensure that online product images accurately represent the items being sold.

How It Works: AI-Based Color Detection and Classification

The paper presents two approaches: a non-AI-based color detection method and an AI-driven classification model.

1. Non-AI-Based Approach

This method utilizes Python libraries such as PIL (Python Imaging Library) and NumPy to calculate the average color saturation of an image. If a product image’s saturation exceeds a predefined threshold, the system flags it as potentially modified.

Python Code for Non-AI-Based Color Detection:

from PIL import Image, ImageStat

import numpy as np

def calculate_saturation(image):

    hsv_image = image.convert(‘HSV’)

    np_img = np.array(hsv_image)

    saturation_channel = np_img[:, :, 1]

    avg_saturation = np.mean(saturation_channel)

    return avg_saturation

def check_color_modification(image_path):

    try:

        image = Image.open(image_path)

        avg_saturation = calculate_saturation(image)

        print(f”Average Saturation: {avg_saturation}”)

        saturation_threshold = 50  # Threshold for color change detection

        if avg_saturation > saturation_threshold:

            print(“Image color seems modified.”)

        else:

            print(“Image color does not seem modified.”)

    except Exception as e:

        print(f”Error: {e}”)

# Usage

image_path = input(“Enter image file path: “)

check_color_modification(image_path)

Limitation: This method cannot analyze complex patterns or adapt dynamically.

2. AI-Based Machine Learning Approach

This method implements a CNN model trained on a dataset of labeled images (original vs. modified). The deep learning model detects subtle color changes and classifies images accurately.

Machine Learning Workflow:

1. Data Collection: Labeled images (original vs. modified) are gathered and pre-processed.
2. Feature Extraction: A CNN model extracts color features to detect modifications.
3. Model Training: The model is trained on the dataset to classify images effectively.
4. Prediction and Deployment: New images are analyzed in real time, providing instant feedback.

CNN-Based Color Classification Model (Python Implementation)

import tensorflow as tf

from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense

from tensorflow.keras.preprocessing import image

import numpy as np

# CNN Model Architecture

model = Sequential([

    tf.keras.layers.Input(shape=(128, 128, 3)),

    Conv2D(32, (3, 3), activation=’relu’),

    MaxPooling2D(pool_size=(2, 2)),

    Conv2D(64, (3, 3), activation=’relu’),

    MaxPooling2D(pool_size=(2, 2)),

    Flatten(),

    Dense(64, activation=’relu’),

    Dense(1, activation=’sigmoid’)

])

# Compile the Model

model.compile(optimizer=’adam’, loss=’binary_crossentropy’, metrics=[‘accuracy’])

# Load and Predict Image Classification

def predict_image_modification(img_path, model):

    img = image.load_img(img_path, target_size=(128, 128))

    img_array = image.img_to_array(img) / 255.0

    img_array = np.expand_dims(img_array, axis=0)

    prediction = model.predict(img_array)

    return “Modified” if prediction >= 0.5 else “Original”

# Example Usage

image_path = ‘test_image.jpg’

print(predict_image_modification(image_path, model))

Advantage: CNN-based models offer over 90% accuracy, dynamically adapting to new data.

Use Case: AI-Powered Image Authenticity in E-Commerce

Problem:

E-commerce platforms face product misrepresentation when sellers modify images to enhance attractiveness. This leads to customer dissatisfaction, increased return rates, and negative reviews.

Solution:

An AI-powered image authenticity verification system integrated into an e-commerce platform detects and flags color-modified images in real time.

System Workflow:

✔ For Sellers:

• When a seller uploads an image, the AI model analyzes the colors.
• If significant modifications are detected, a warning prompt suggests verifying image accuracy before listing.

✔ For Buyers:

• If a flagged image appears in search results, a warning label is displayed.
• Buyers can verify product authenticity by reviewing product descriptions or contacting the seller.

AI-Powered E-Commerce Image Validation Code

def alert_seller(image_path):

    status = predict_image_modification(image_path, model)

    if status == “Modified”:

        print(“Warning: The uploaded image may have been modified. Please verify before listing.”)

    else:

        print(“Image verification complete. No modifications detected.”)

def alert_buyer(image_path):

    status = predict_image_modification(image_path, model)

    if status == “Modified”:

        print(“Notice: This product image may have been altered. Please verify with the seller.”)

Results and Impact of AI on Color Management

1. Increased Transparency: Buyers receive accurate product visuals.
2. Seller Compliance: Encourages genuine product representation.
3. Improved Consumer Trust: Enhances satisfaction and reduces return rates.
4. Sales Optimization: AI-optimized colors boost engagement and conversions.

Experimental Findings:

• CNN-based AI achieved over 90% accuracy in detecting modifications.
• Consumer trust improved, reducing returns and negative reviews.
• Sellers benefited from AI-assisted transparency, leading to higher conversions.

Conclusion & Future Enhancements

This research proves that AI-driven color variation management is a game-changer for product design and e-commerce authenticity. Implementing machine learning models ensures that product images reflect reality, boosting consumer confidence.

Future Improvements:

Enhanced AI Models: Deeper CNN architectures for more accurate predictions.
Real-Time Integration: AI-based live recommendations for sellers.
Expanded Features: Detect additional image modifications like texture changes.

For more details, refer to the full paper:
DOI: https://everant.org/index.php/etj/article/view/1616/1163