  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.
