Case Study

Supercharging Image Retrieval System with Deep Learning & AI for Marketers

Industry: [CATEGORY]

Client introduction: A Well-Known Digital Marketing Agency

Tools and Technologies:

Python

NLTK (Natural Language Toolkit)

FER (Facial expression recognition)

DETR (DEtection TRansformer) Model

CLIP Model

HAAR Cascade

Mask RCNN

PyTorch

TensorFlow

OpenCV

HuggingFace Transformers

NUMPY

PANDAS

BeautifulSoup

Boto3

FLASK Framework

AWS SQS

AWS S3 Bucket

Kubernetes / Docker

AWS SageMaker

AWS EC2

AWS RDS

PHP

Laravel

AngularJS

MySQL

Shutterstock API

ImagesBazaar API

FreePik API

Visual Studio Code

Jupyter Notebook

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Business Challenge

Getting lost in the sea of visual content, the client struggled to precisely locate relevant images. As a reputed brand in the advertising and media industry, they possess a massive internal database that contains thousands of images including real photos/pictures, cartoons, line art, sketches, and the like. The database includes a diverse range of subjects, topics, and types depicting specific moods to be used for specific purposes such as education, marketing, sales, and simply to showcasing.

The traditional keyword-based searches proved inadequate as they showed limitations of human-generated captions. The digital marketing agency sought a powerful AI-based solution that bridges the gap between textual prompts and image content of all kinds.

Quick Onboarding

The agency contacted WeblineIndia with their concern. Our business and technology consultants got right into the core of the issue and suggested the roadmap to having a customized AI based image retrieval system that would solve the problem once and for all. The project was discussed, and AI developers got onboard in less than a week.

The Solution

A custom-built AI solution for image retrieval system was suggested. The tool would be powered by the dynamic duo of NLP (Natural Language Processing) and Computer Vision along with some help of other contemporary technologies.

Unpacking the tech stack

The client had a clear idea about utilizing Artificial Intelligence; nevertheless, we added a few more technologies and tools to make the software product more powerful and accurate. Here are the primary tech stacks used in the project:

AI Tech Stack for Image Retrieval System

Natural Language Processing

NLP is a branch of AI that empowers machines to learn and manipulate human language. Our AI ML developers deployed Recurrent Neural Network (RNN), specifically an LSTM (Long Short-Term Memory) network. This helped us analyze the textual descriptions fed by users. LSTMs is an excellent network to capture the sequential nature of the languages. Such features make LSTMs decipher the context and its relationship with words in the vicinity of users’ query and intention.

Computer Vision

Computer Vision is used to give eyes to machines so that they can ‘see’, ‘grasp,’ and ‘interpret’ the visual inputs or data. Our AI engineers used CNN (Convolutional Neural Network) architecture that extracted meaningful features from the images uploaded by users or those with a humongous database of visual content. CNNs are supersonic at identifying the patterns and recognizing objects along with their colors/shades, textures, and spatial connections with visual graphics.

Python programming

Known for its reliability and extensive libraries, we chose Python as the foundation of building a robust AI system. Libraries such as TensorFlow and PyTorch are known to construct and train neural networks to possess intelligence near to human for recognizing text, picture, and other visual content. Huggingface Transformer libraries were used for easy AI Model management and pipelining of the application.

Amazon Web Services

AWS was selected as a cloud platform because of the scalability and processing capabilities it offers. AWS easily handled client’s extensive collection of images, texts, and other visual content. AWS cloud platform also proved to be the best tool because of its computationally intensive power of deep learning algorithms.

Behind The Scenes: The deep dive into the core functionality

The client had a clear idea about utilizing Artificial Intelligence; nevertheless, we added a few more technologies and tools to make the end product more powerful and accurate.

Behind the scenes of Image retrieval system

However, our AI based Image retrieval system required a bit more exclusive approach with contemporary tools and technologies.

Feature Extraction with CNNs

When a user uploads a picture or inserts a prompt, CNN is triggered to perform its tasks. The powerful neural network swiftly analyzes the image and meticulously dissects it into tinier sections while abstracting the rich tapestry of functions. These functions include a spectrum of visual content such as presence of specific objects (say, a car or a laptop), dominant shades (such as green, red), textures (like rough or smooth), and the spatial arrangement of these components within the picture.

NLP steps in; LSTMs to generate captions

The baton is then passed to the LSTM network which is a NLP powerhouse leveraging the previously extracted information to build a comprehensive caption surpassing the basic keywords in both accuracy and speed. The LSTM generates captions by analyzing the intricate connection between the features. As a result, the caption generated is the picture’s essence that has description, emotion, activity, and mood of the visual content.

Keyword Extraction

Unveiling the semantic core, the system then delves deeper into caption generation. It does it through deployment of NLP techniques that identify key entities and gist. These keywords are taken as a foundation to search the image database.

Image retrieval matching the content

Finally, the extracted keywords are parsed and matched through the enormous database or the repository. An AI-powered retrieval algorithm takes the center stage to compare the keywords from inputs to the captions linked with each visual content or image in the database. Images that contain captions with a high degree of relevance and semantic similarity to the queries put are then presented as a result. Images without captions also are parsed for their content such as colors, object identification, and the overall mood, sentiment, emotion of the picture subject. Ultimately, the results are retrieved images matching closely to the users’ intent.

Benefits Beyond Efficiency

Precision over keywords

The AI-enabled system transcends the limitations of obsolete techniques of keyword-based searches and results. The powerful AI system delves into semantic meaning of both the image content and user’s queries to bring results that are remarkably more accurate and precise.

Unleashing creativity

The AI-powered image retrieval software opened the doors to explore newer creative avenues for the agency because it identified images with similar content but aesthetically different ones. The marketers could discover unimaginable visual connections and sparked tremendous unique campaign ideas resonating with the targeted audience.

Future-ready scalability

Built with powerful AWS cloud platform, the image retrieval system efficiently scales to accommodate the ever-growing database of visual and textual content. The software tool is future ready as it ensures its effectiveness is not vulnerable to any expansion of visual libraries in the coming times.

Quantifiable results

With the help of AI-powered image retrieval software, the agency implemented a much-required solution for their business. There were exceptional transformational results for the client’s agency.

75% reduction in image search time: the marketers at the agency were capable of locating relevant pictures with supersonic speed and thus saved valuable time to spare on other strategic campaign planning.

40% increase in client satisfaction: the agency noted that they were able to deliver consistently high-quality campaigns with very enticing and engaging graphics. The campaigns with related visual content resulted in boosting client satisfaction and thus loyalty.

28% increase in campaign effectiveness: the time saved from searching relevant images was spent in strategizing marketing with more aggressive efforts. The workflow was improved and as a result, the agency could optimize better resource allocation.

Conclusion

By harnessing the power of AI in image retrieval, this case study showcases how AI can reshape various industries, specially marketing & advertising industry. This technology not only personalizes campaigns but also unlocks a new level of efficiency, paving the way for a future of smarter visual marketing.

If you want to take advantage of AI, then contact us today for a Free consultation.