Concept

Concept is a technique that leverages CLIP and BERTopic-based techniques to perform Concept Modeling on images.

Since topics are part of conversations and text, they do not represent the context of images well. Therefore, these clusters of images are
referred to as ‘Concepts’ instead of the traditional ‘Topics’.

Thus, Concept Modeling takes inspiration from topic modeling techniques
to cluster images, find common concepts and model them both visually
using images and textually using topic representations.

Installation

Installation, with sentence-transformers, can be done using pypi:

pip install concept

Quick Start

First, we need to download and extract 25.000 images from Unsplash used in the sentence-transformers
example:

import os
import zipfile
from tqdm import tqdm
from PIL import Image
from sentence_transformers import util


# 25k images from Unsplash
img_folder = 'photos/'
if not os.path.exists(img_folder) or len(os.listdir(img_folder)) == 0:
    os.makedirs(img_folder, exist_ok=True)
    
    photo_filename = 'unsplash-25k-photos.zip'
    if not os.path.exists(photo_filename):   #Download dataset if does not exist
        util.http_get('http://sbert.net/datasets/'+photo_filename, photo_filename)
        
    #Extract all images
    with zipfile.ZipFile(photo_filename, 'r') as zf:
        for member in tqdm(zf.infolist(), desc='Extracting'):
            zf.extract(member, img_folder)
images = [Image.open("photos/"+filepath) for filepath in tqdm(img_names)]

Next, we only need to pass images to Concept:

from concept import ConceptModel
concept_model = ConceptModel()
concepts = concept_model.fit_transform(images)

The resulting concepts can be visualized through concept_model.visualize_concepts():

However, to get the full experience, we need to label the concept clusters with topics. To do this,
we need to create a vocabulary:

from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import TfidfVectorizer
docs = fetch_20newsgroups(subset='all',  remove=('headers', 'footers', 'quotes'))['data']
vectorizer = TfidfVectorizer(ngram_range=(1, 2)).fit(docs)
words = vectorizer.get_feature_names()
words = [words[index] for index in np.argpartition(vectorizer.idf_, -50_000)[-50_000:]]

Then, we can pass in the resulting words to Concept:

from concept import ConceptModel

concept_model = ConceptModel()
concepts = concept_model.fit_transform(images, docs=words)

Again, the resulting concepts can be visualized. This time however, we can also see the generated topics
through concept_model.visualize_concepts():