README

What is this repository for?

• Code suplementing FEDCSIS MIDI 2017 paper "What Looks Good with my Sofa: Multimodal Search Engine for Interior Design" by Ivona Tautkute, Aleksandra Możejko, Wojciech Stokowiec, Tomasz Trzciński, Łukasz Brocki and Krzysztof Marasek

@inproceedings{FedCSIS201756,
	author={Ivona Tautkute and Aleksandra Możejko and Wojciech Stokowiec and Tomasz Trzciński and Łukasz Brocki and Krzysztof Marasek,},
	pages={1275--1282},
	title={What Looks Good with my Sofa: Multimodal Search Engine for Interior Design},
	booktitle={Proceedings of the 2017 Federated Conference on Computer Science and Information Systems},
	year={2017},
	editor={M. Ganzha and L. Maciaszek and M. Paprzycki},
	publisher={IEEE},
	doi={10.15439/2017F56},
	url={http://dx.doi.org/10.15439/2017F56},
	volume={11},
	series={Annals of Computer Science and Information Systems}
}

Dependencies

• Installation of Open CV 2
• Python 3

Installation

Docker

• Build docker container docker build -t container-name .
• Run on port 3000 docker run -p 3000:3000 -it container-name

Locally

• Download YOLO weights (https://drive.google.com/open?id=0BywyiovWX-UkeGNxdkxKdDMtdDg)
• Install Open CV and requirements from requirements.txt
• Run the app python3 run.py

Web app

• Start on localhost by running run.py

• Configuration of Flask app interface: app/web_interface.py

Notebooks

• Results for IKEA Dataset - contains accuracy calculations for visual search, recall curve on IKEA dataset
• Interior style dataset benchmark - contains accuracy calculations for visual and textual search on Style dataset
• Results for calculating similarity - contains similarity metric calculations for different text queries and objects in IKEA dataset

Visual Search

• Visual search functions: finder.py
• Visual feature extraction: cnn_feature_extraction.py
• Functions for YOLO object detection: detect_objects.py
• Model parameters: parameters.py

Textual Search

• Query transformation using SVD and finding n-nearest neigbhours: search_engine.py
• Word2vec and Countvect "training": training.py
• tSNE visualization: embedding.py
• blender.py - leftover
• Query transformation using LSTM is in the jupyter notebook sent on style-search channel on slack