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  1. Proceedings of the international workshop on Very-large-scale multimedia corpus, mining and retrieval (VLS-MCMR '10)
  2. Towards extensible automatic image annotation with the bag-of-words approach
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Incremental multi-classifier learning algorithm on grid'5000 for large scale image annotation
Commercial film detection and identification based on a dual-stage temporal recurrence hashing algorithm
Towards extensible automatic image annotation with the bag-of-words approach
Automatic image annotation by using relevant keywords extracted from auxiliary text documents
A large-scale performance study of cluster-based high-dimensional indexing
An analysis of user behavior in online video streaming
A deep-learning model-based and data-driven hybrid architecture for image annotation
Optimizing similarity-based image joins in a multimedia database
Weighting informativeness of bag-of-visual-words by kernel optimization for video concept detection
Concept detector refinement using social videos

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Towards extensible automatic image annotation with the bag-of-words approach

Content Provider ACM Digital Library
Author Meyer-Wegener, Klaus Nagy, Robert
Abstract Visual-word-based image categorization has proven to be very effective in several publications and contests. Recently, various approaches have been proposed to address the need for scalability and computational performance of classification based on Bag of Words. Despite these efforts, extensibility still remains an issue. Classifiers and histograms of visual words are both heavily dependent on an immutable general visual vocabulary created during the training step based on training images. Adding a new category that is insufficiently represented by the visual words in the vocabulary requires recreation of the visual vocabulary, complete recomputation of histograms and retraining of classifiers. When adding a new category, current approaches need to fully rebuild the whole recognition system. We address the problem of extensibility by combining class-specific vocabularies with outlier visual words. Classification is achieved by computing a scoring function for each class-specific vocabulary and selecting the highest score value. We show first results of our highly parallelizable and distributable approach on the Caltech 256 dataset.
Starting Page 43
Ending Page 48
Page Count 6
File Format PDF
ISBN 9781450301664
DOI 10.1145/1878137.1878148
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2010-10-29
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Bag of words Extensibility Object recognition
Content Type Text
Resource Type Article
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