MindFinder: Interactive Sketch-based Image Search on Millions of Images

In this paper, we showcase the MindFinder system, which is
an interactive sketch-based image search engine. Different
from existing work, most of which is limited to a small scale
database or only enables single modality input, MindFinder
is a sketch-based multimodal search engine for million-level
database. It enables users to sketch major curves of the
target image in their mind, and also supports tagging and
coloring operations to better express their search intentions.
Owning to a friendly interface, our system supports multiple
actions, which help users to flexibly design their queries. After
each operation, top returned images are updated in real
time, based on which users could interactively refine their
initial thoughts until ideal images are returned. The novelty
of the MindFinder system includes the following two aspects:
1) A multimodal searching scheme is proposed to retrieve
images which meet users’ requirements not only in structure,
but also in semantic meaning and color tone. 2) An
indexing framework is designed to make MindFinder scalable
in terms of database size, memory cost, and response
time. By scaling up the database to more than two million
images, MindFinder not only helps users to easily present
whatever they are imagining, but also has the potential to
retrieve the most desired images in their mind.