Impact of edges characterization on image clustering

Costantini, Luca; Capodiferro, Licia; Carli, Marco; Neri, Alessandro

doi:10.1109/euvip.2010.5699117

Cited by 2 publications

(3 citation statements)

References 8 publications

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“…The graph shows a good value of percentage of impure clusters for an average number of cluster of 5,9 that improves up to 15,6% increasing the average number of clusters up to 9. Referring to previous works of the authors [12], [13] , it is important to notice that in the present case the clustering algorithm is performed on natural images, rather than texture datasets.…”

Section: Resultsmentioning

confidence: 98%

“…In this work the edge characterization is performed in two step. In the first step the main edges are selected and in the second step the histogram of their phase is computed [13] .…”

Section: Edges Featurementioning

confidence: 99%

“…If the query image is not well-chosen the images that the system does not rank in the first positions could be very important. To overcame this drawback we use a clustering algorithm [12], [13] . Its aim is to group the image in cluster and show to the user only one representative image for each cluster.…”

Section: Clustering Algorithmmentioning

confidence: 99%

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Features extraction based on Fisher's information

et al. 2011

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"In this paper a novel scheme for extracting the global features from an image. Usually the features are extracted from the whole image. In the proposed approach, only the image regions conveying information are considered. The two steps procedure is based on the Fisher's information evaluation computed by linear combination of Zernike expansion coefficients. Then, by using the region growing algorithm, only high information rate regions are considered. The considered features are texture, edges, and color. The performances of the proposed scheme has been evaluated by using the retrieval rate. Experimental results show an increase in the retrieval rate with respect to use the same features computed on whole image.

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Section: Resultsmentioning

confidence: 98%

“…In this work the edge characterization is performed in two step. In the first step the main edges are selected and in the second step the histogram of their phase is computed [13] .…”

Section: Edges Featurementioning

confidence: 99%

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Features extraction based on Fisher's information

et al. 2011

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SVM for historical sport video classification

Capodiferro

Costantini

Mangiatordi

et al. 2012

2012 5th International Symposium on Communications, Control and Signal Processing

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""In this work the authors propose a classification method based on Support Vector Machine (SVM) and key frames features extraction to classify historical sport video contents. In the context of the Italian Project, IRMA (Information Retrieval in Multimedia Archives), with the goal to recover and preserve historical videos of proven cultural interest, a data set made up of several hours of videos from the 1960 Olympic games, provided by RAI and Teche RAI, is adopted as testbed. Each video is summarized by its key frames and represented by the features vectors computed in the Laguerre Gauss transformed domain. The high-level video classification starts from these vectors that are the input of the SVM classifier. The experimental results show the effectiveness of the proposed method."

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Impact of edges characterization on image clustering

Cited by 2 publications

References 8 publications

Features extraction based on Fisher's information

Features extraction based on Fisher's information

SVM for historical sport video classification

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