Interest point and segmentation-based photo annotation

Authors:
Bálint Daróczy;István Petrás;András A. Benczúr;Zsolt Fekete;Dávid Nemeskey;Dávid Siklósi;Zsuzsa Weiner
Affiliations:
Data Mining and Web search Research Group, Informatics Laboratory, Computer and Automation Research Institute of the Hungarian Academy of Sciences;Data Mining and Web search Research Group, Informatics Laboratory, Computer and Automation Research Institute of the Hungarian Academy of Sciences;Data Mining and Web search Research Group, Informatics Laboratory, Computer and Automation Research Institute of the Hungarian Academy of Sciences;Data Mining and Web search Research Group, Informatics Laboratory, Computer and Automation Research Institute of the Hungarian Academy of Sciences;Data Mining and Web search Research Group, Informatics Laboratory, Computer and Automation Research Institute of the Hungarian Academy of Sciences;Data Mining and Web search Research Group, Informatics Laboratory, Computer and Automation Research Institute of the Hungarian Academy of Sciences;Data Mining and Web search Research Group, Informatics Laboratory, Computer and Automation Research Institute of the Hungarian Academy of Sciences
Venue:
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
Year:
2009

Citing 12
Cited 1

Relevance weighting of search terms

Document retrieval systems
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying

IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition from Local Scale-Invariant Features

ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Region-based image retrieval using integrated color, shape, and location index

Computer Vision and Image Understanding - Special issue on color for image indexing and retrieval
Image Categorization by Learning and Reasoning with Regions

The Journal of Machine Learning Research
Image similarity search with compact data structures

Proceedings of the thirteenth ACM international conference on Information and knowledge management
Term proximity scoring for ad-hoc retrieval on very large text collections

SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
LIBLINEAR: A Library for Large Linear Classification

The Journal of Machine Learning Research
Term proximity scoring for keyword-based retrieval systems

ECIR'03 Proceedings of the 25th European conference on IR research
Diversity in photo retrieval: overview of the ImageCLEFPhoto task 2009

CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
Overview of the wikipediaMM task at ImageCLEF 2009

CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
Overview of the CLEF 2009 large-scale visual concept detection and annotation task

CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments

Overview of the CLEF 2009 large-scale visual concept detection and annotation task

CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments

Quantified Score

Hi-index	0.00

Visualization

Abstract

Our approach to the ImageCLEF 2009 tasks is based on image segmentation, SIFT keypoints and Okapi BM25-based text retrieval. We use feature vectors to describe the visual content of an image segment, a keypoint or the entire image. The features include color histograms, a shape descriptor as well as a 2D Fourier transform of a segment and an orientation histogram of detected keypoints. We trained a Gaussian Mixture Model (GMM) to cluster the feature vectors extracted from the image segments and keypoints independently. The normalized Fisher gradient vector computed from GMM of SIFT descriptors is a well known technique to represent an image with only one vector. Novel to our method is the combination of Fisher vectors for keypoints with those of the image segments to improve classification accuracy. We introduced correlation-based combining methods to further improve classification quality.