Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/20920
Title: Data-driven scene understanding by adaptive exemplar retrieval
Authors: Liu, X
Yang, W
Wang, Q
Lin, L
Lai, JH
Keywords: Multi-image graphical model
Scene understanding
Semantic segmentation
Semantic-aware sparse coding
Issue Date: 2015
Publisher: IEEE Computer Society
Source: Proceedings - IEEE International Conference on Multimedia and Expo, 2015, 6890162 How to cite?
Abstract: This article studies a data-driven approach for semantically scene understanding, without pixelwise annotation and classifier pre-training. Our framework parses a target image with two steps: (i) retrieving its exemplars (i.e. references) from an image database, where all images are unsegmented but annotated with tags; (ii) recovering its pixel labels by propagating semantics from the references. We present a novel framework making the two steps mutually conditional and bootstrapped under the probabilistic Expectation-maximization (EM) formulation. In the first step, the references are selected by jointly matching their appearances with the target as well as the semantics. We process the second step via a combinatorial graphical representation, in which the vertices are superpixels extracted from the target and its selected references. Then we derive the potentials of assigning labels to one vertex of the target, which depends upon the graph edges that connect the vertex to its spatial neighbors of the target and to its similar vertices of the references. Two steps can be both solved analytically, and the inference is conducted in a self-driven fashion. In the experiments, we validate our approach on two public databases, and demonstrate superior performances over the state-of-the-art methods.
Description: 2014 IEEE International Conference on Multimedia and Expo, ICME 2014, 14-18 July 2014
URI: http://hdl.handle.net/10397/20920
ISSN: 1945-7871
DOI: 10.1109/ICME.2014.6890162
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