Accession Number:

AD1039805

Title:

Style-aware Mid-level Representation for Discovering Visual Connections in Space and Time (Open Access)

Personal Author(s):

Corporate Author:

Carnegie Mellon University Pittsburgh United States

Report Date:

2014-03-03

Abstract:

We present a weakly-supervised visual data mining approach that discovers connections between recurring mid-level visual elements in historic temporal and geographic spatial image collections, and attempts to capture the underlying visual style. In contrast to existing discovery methods that mine for patterns that remain visually consistent throughout the dataset, our goal is to discover visual elements whose appearance changes due to change in time or location i.e., exhibit consistent stylistic variations across the label space date or geo-location. To discover these elements, we first identify groups of patches that are style-sensitive. We then incrementally build correspondences to find the same element across the entire dataset. Finally, we train style-aware regressors that model each elements range of stylistic differences. We apply our approach to date and geo-location prediction and show substantial improvement over several baselines that do not model visual style. We also demonstrate the methods effectiveness on the related task of fine-grained classification.

Descriptive Note:

Conference Paper

Supplementary Note:

2013 IEEE International Conference on Computer Vision (ICCV) , 01 Dec 2013, 08 Dec 2013,

Pages:

0008

Descriptors:

Identifiers:

Subject Categories:

Communities Of Interest:

Modernization Areas:

Distribution Statement:

Approved For Public Release;

File Size:

2.92MB

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