SCHEDULE: NOV 16-22, 2013
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Caranx: Scalable Social Image Index Using Phylogenetic Tree of Hashtags
SESSION: Research Poster Reception
EVENT TYPE: Posters, Electronic Posters, and Education Posters
TIME: 5:15PM - 7:00PM
AUTHOR(S):Yusheng Xie, Zhuoyuan Chen, Ankit Agrawal, Wei-keng Liao, Alok Choudhary
ROOM:Mile High Pre-Function
ABSTRACT:
Most existing image indexing techniques rely on Scale Invariant Feature Transformation (SIFT) for extracting local point features. Applied to individual image, SIFT extracts hundreds of numerical vectors. The vectors are quantized and stored in tree-like data structures for fast search. SIFT-based indexing can exhibit weakness under certain non-rigid transformations, which are common among real world applications. For example, SIFT often cannot recognize a face as the same with different expressions (e.g. giggling vs. crying). Non-Rigid Dense Correspondence (NRDC) addresses such drawbacks of SIFT. However, directly using NRDC incurs an impractical amount of computation in large-scale image indexing. We present a novel idea here that uses social hashtags to organize the images into a phylogenetic tree (PT). We provide an efficient algorithm to build/search the PT, and show that using PT structure can effectively avoid unnecessary NRDC computation. The resulting image index provides more accurate and diversified search results.
Chair/Author Details:
Yusheng Xie - Northwestern University
Zhuoyuan Chen - Adobe Research
Ankit Agrawal - Northwestern University
Wei-keng Liao - Northwestern University
Alok Choudhary - Northwestern University
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