ImageSpirit: Verbal Guided Image Parsing

TL;DR

This paper introduces ImageSpirit, a system that uses verbal instructions to parse images into pixel-wise object and attribute labels, enabling natural, hands-free image interaction.

Contribution

It formulates image parsing as joint object and attribute labeling and enables verbal refinement, offering a novel natural interaction modality for image understanding.

Findings

Effective verbal-guided image parsing demonstrated on real-world images.

System enables hands-free, natural interaction for image annotation.

Quantitative and user study results validate system performance.

Abstract

Humans describe images in terms of nouns and adjectives while algorithms operate on images represented as sets of pixels. Bridging this gap between how humans would like to access images versus their typical representation is the goal of image parsing, which involves assigning object and attribute labels to pixel. In this paper we propose treating nouns as object labels and adjectives as visual attribute labels. This allows us to formulate the image parsing problem as one of jointly estimating per-pixel object and attribute labels from a set of training images. We propose an efficient (interactive time) solution. Using the extracted labels as handles, our system empowers a user to verbally refine the results. This enables hands-free parsing of an image into pixel-wise object/attribute labels that correspond to human semantics. Verbally selecting objects of interests enables a novel and natural interaction modality that can possibly be used to interact with new generation devices (e.g. smart phones, Google Glass, living room devices). We demonstrate our system on a large number of real-world images with varying complexity. To help understand the tradeoffs compared to traditional mouse based interactions, results are reported for both a large scale quantitative evaluation and a user study.