&lt;title&gt;Image annotation using SVM&lt;/title&gt;

Cusano, Claudio; Ciocca, Gianluigi; Schettini, Raimondo

doi:10.1117/12.526746

Cited by 193 publications

(104 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Methods of the second category employ trained classifiers to find correlations between the words and the annotated image's visual features. We can mention here Bayes Point Machine (Chang et al 2003), Support Vector Machine (Cusano et al 2004) and Decision Trees (Kwasnicka and Paradowski 2008) which all estimate the visual features distributions associated with each word. Some authors try to refine the annotation results by reducing the difference between the expected and resulting word count vectors (Kwasnicka and Paradowski 2006), by using Word-Net which contains semantic relations between words (Jin et al 2005) or by word co-occurrence models coupled with fast random walks (Llorente et al 2009), an interesting approach exploiting the recent advances in graph processing.…”

Section: Related Approachesmentioning

confidence: 99%

Image auto-annotation with automatic selection of the annotation length

2012

View full text Add to dashboard Cite

Developing a satisfactory and effective method for auto-annotating images that works under general conditions is a challenging task. The advantages of such a system would be manifold: it can be used to annotate existing, large databases of images, rendering them accessible to text search engines; or it can be used as core for image retrieval based on a query image's visual content. Manual annotation of images is a difficult, tedious and time consuming task. Furthermore, manual annotations tend to show great inter-person variance: considering an image, the opinions about what elements are significant and deserve an annotation vary strongly. The latter poses a problem for the evaluation of an automatic method, as an annotation's correctness is greatly subjective. In this paper we present an automatic method for annotating images, which addresses one of the existing methods' major limitation, namely a fixed annotation length. The proposed method, PATSI, automatically chooses the resulting annotation's length for each query image. It is held as simple as possible and a build-in parameter optimization procedure renders PATSI de-facto parameter free. Finally, PATSI is evaluated on standard datasets, outperforming various state-of-the-art methods.

show abstract

Section: Related Approachesmentioning

confidence: 99%

Image auto-annotation with automatic selection of the annotation length

2012

View full text Add to dashboard Cite

show abstract

“…supervised learning by Support Vector Machines (SVMs) can be used to classify images and image parts to a number of concepts [6]. Another approach is it to look at the probability of words associated with image features [5,17].…”

Section: Related Workmentioning

confidence: 99%

Automatic image annotation using visual content and folksonomies

Lindstaedt

Mörzinger

Sorschag

et al. 2008

Multimed Tools Appl

View full text Add to dashboard Cite

Abstract. Automatic image annotation is an important and challenging task when managing large image collections. This paper describes techniques for automatic image annotation by taking advantage of collaboratively annotated image databases, so called visual folksonomies. Our approach applies two techniques based on image analysis: Classification annotates images with a controlled vocabulary while tag propagation uses user generated, folksonomic annotations and is therefore capable of dealing with an unlimited vocabulary. Experiments with a pool of Flickr images demonstrate the high accuracy and efficiency of the proposed methods in the task of automatic image annotation.

show abstract

“…The classification approaches for automatic image annotation treat each annotated word as an independent class and create a different image classification model for every word. Work such as linguistic indexing of pictures [9], image annotation using SVM [8] and Bayes point machine [7] belong to this category. Recently, relevance language models [10] - [12] have been successfully applied to automatic image annotation.…”

Section: Related Workmentioning

confidence: 99%

Automated Image Annotation for Semantic Indexing and Retrieval of Medical Images

Krishna¹,

Prasad²

2012

IJCA

View full text Add to dashboard Cite

Medical image retrieval to search for clinically relevant and visually similar images depicting suspecious lesions have been attracting research interest. Content-based image retrieval (CBIR) is an important alternate and complement to traditional text-based retrieval using keywords. We have implemented CBIR system based on effective use of texture information within the images obtained by statistical cooccurrence matrix method. Also, the method is improved by bridging the semantic gap between low-level visual features and the high-level semantic concepts using automated image annotations. In this paper, we have proposed a classification-based multi-class multi-label semantic model and the corresponding learning procedure to address the problem of automatic image annotation using J48 decision tree classifier and show its application to medical image retrieval. Hash structure is used to index images. Eucledian distance measure is used for similarity measurement. Both the methods are compared using precision and recall measures. Semantic indexing is shown to outperform CBIR for MR-T2 axial brain images.

show abstract

<title>Image annotation using SVM</title>

Cited by 193 publications

References 16 publications

Image auto-annotation with automatic selection of the annotation length

Image auto-annotation with automatic selection of the annotation length

Automatic image annotation using visual content and folksonomies

Automated Image Annotation for Semantic Indexing and Retrieval of Medical Images

Contact Info

Product

Resources

About