# Approximate Query Matching for Image Retrieval

arXiv: https://arxiv.org/abs/1803.05401 · HTML: https://mlsanity.com/paper/1803.05401/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2018.

## Built on by (most cited first, 1 of 1)

- [A multimodal deep learning framework for scalable content based visual media retrieval](https://mlsanity.com/paper/2105.08665/) (2021)

## Abstract (arXiv)

Traditional image recognition involves identifying the key object in a portrait-type image with a single object focus (ILSVRC, AlexNet, and VGG). More recent approaches consider dense image recognition - segmenting an image with appropriate bounding boxes and performing image recognition within these bounding boxes (Semantic segmentation). The Visual Genome dataset [5] is an attempt to bridge these various approaches to a cohesive dataset for each subtask - bounding box generation, image recognition, captioning, and a new operation: scene graph generation. Our focus is on using such scene graphs to perform graph search on image databases to holistically retrieve images based on a search criteria. We develop a method to store scene graphs and metadata in graph databases (using Neo4J) and to perform fast approximate retrieval of images based on a graph search query. We process more complex queries than single object search, e.g. "girl eating cake" retrieves images that contain the specified relation as well as variations.