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  1. Proceedings of the 4th ACM/IEEE international workshop on Analysis and retrieval of tracked events and motion in imagery stream (ARTEMIS '13)
  2. A non-parametric unsupervised approach for content based image retrieval and clustering
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On improving the robustness of variational optical flow against illumination changes
A non-parametric unsupervised approach for content based image retrieval and clustering
Abnormal crowd behavior detection and localization using maximum sub-sequence search
Hand gesture recognition with depth data
Warping trajectories for video synchronization
Behavior recognition from video based on human constrained descriptor and adaptable neural networks
Nobody likes Mondays: foreground detection and behavioral patterns analysis in complex urban scenes
Background modeling methods for visual detection of maritime targets
Domain transfer for person re-identification
Cross-domain traffic scene understanding by motion model transfer

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A non-parametric unsupervised approach for content based image retrieval and clustering

Content Provider ACM Digital Library
Author Doulamis, Nikolaos Doulamis, Anastasios Makantasis, Konstantinos
Abstract Nowadays, there are available extremely large collections of images located on distributed and heterogeneous platforms over the web. The proliferation of billions of shared photos has outpaced the current technology for browsing such collections, but at the same time it spurred the emergence of new image retrieval techniques based not only on photos' visual information, but on geo-location tags and camera exif data. Although, additional image information may be proven very useful for preliminary image retrieval, the final retrieved result is necessary to be refined by exploiting visual information. In this paper we present a process for refining image retrieval results by exploiting and fusing two unsupervised clustering techniques: DBSCAN and spectral clustering. DBSCAN algorithm is used to remove outliers from the initially retrieved image set, and spectral clustering finalizes retrieval process by clustering together visually similar images. However, DBSCAN and spectral clustering require manual tunning of their parameters, which usually requires a priori knowledge of the dataset. To overcome this problem we developed a tuning mechanism that automatically tunes the parameters of both algorithms. For the evaluation of the proposed approach we used thousands of images from Flickr downloaded using text queries for well known cultural heritage monuments.
Starting Page 33
Ending Page 40
Page Count 8
File Format PDF
ISBN 9781450323932
DOI 10.1145/2510650.2510656
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2013-10-21
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Image retrieval Image clustering Spectral clustering Local descriptors Dbscan
Content Type Text
Resource Type Article
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