ImageSpirit: Verbal guided image parsing

ImageSpirit: Verbal guided image parsing

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 l...

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Bibliographic Details
Main Authors: CHENG, Ming-Ming, ZHENG, Shuai, LIN, Wen-yan, VINEET, Vibhav, STURGESS, Paul, CROOK, Nigel, MITRA, Niloy J., TORR, Philip
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2014
Subjects:
Design
Human Factors
Languages
Image parsing
natural language control
speech interface
object class segmentation
image parsing
visual attributes
multilabel CRF
Graphics and Human Computer Interfaces
Online Access:https://ink.library.smu.edu.sg/sis_research/4854
https://ink.library.smu.edu.sg/context/sis_research/article/5857/viewcontent/ImageSpirit__Verbal_Guided_Image_Parsing__AV.pdf
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Institution: Singapore Management University
Language: English
Description
Summary: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 pixels. In this article 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 interest enables a novel and natural interaction modality that can possibly be used to interact with new generation devices (e.g., smartphones, Google Glass, livingroom devices). We demonstrate our system on a large number of real-world images with varying complexity. To help understand the trade-offs compared to traditional mouse-based interactions, results are reported for both a large-scale quantitative evaluation and a user study.

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