Paper Info
Title
Image Hub Explorer: Evaluating Representations and Metrics for Content-Based Image Retrieval and Object Recognition.
Abstract
We present a novel tool for image data visualization and analysis, Image Hub Explorer. It is aimed at developers and researchers alike and it allows the users to examine various aspects of content-based image retrieval and object recognition under different built-in metrics and models. Image Hub Explorer provides the tools for understanding the distribution of influence in the data, primarily by examining the emerging hub images. Hubness is an aspect of the well-known curse of dimensionality that hampers the effectiveness of many information systems. Its consequences were thoroughly examined in the context of music/audio search and recommendation, but not in case of image retrieval and object recognition. Image Hub Explorer was made with the goal of raising awareness of the hubness phenomenon and offering potential solutions by implementing state-of-the-art hubness-aware metric learning, ranking and classification methods. Various visualization components allow for a quick identification of critical issues and we hope that they will prove helpful in working with large image datasets. We demonstrate the effectiveness of the implemented methods in various object recognition tasks.
Year
DOI
Venue
2013
10.1007/s11042-014-2254-1
Multimedia Tools Appl.
Keywords
Field
DocType
Image retrieval, Visualization, Hubness, Object recognition, k-nearest neighbors, Machine learning
Information system,Computer vision,Data visualization,Automatic image annotation,Ranking,Visualization,Computer science,Image retrieval,Artificial intelligence,Machine learning,Content-based image retrieval,Cognitive neuroscience of visual object recognition
Conference
Volume
Issue
ISSN
74
24
1573-7721
Citations 
PageRank 
References 
1
0.34
29
Authors
2
Name
Order
Citations
PageRank
Nenad Tomasev1987.60
Dunja Mladenic21484170.14