Image Annotation Refinement Using Web-Based Keyword Correlation

Llorente, Ainhoa; Motta, Enrico; Rüger, Stefan

doi:10.1007/978-3-642-10543-2_22

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…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

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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.

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Section: Related Approachesmentioning

confidence: 99%

Image auto-annotation with automatic selection of the annotation length

2012

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“…In The first category, image annotation is formulated as a supervised classification problem and Different machine learning techniques are used to predict the annotations of new images [3][4][5]. The second category learn the correlation between image features and textual words from the examples of annotated images and then apply the learned correlation to predict words for unseen images [6][7][8][9]. These methods infer the association between the images and their related tags only at the image level, and utilize the image-to-image visual similarity to refine or predict Image tags.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Object Annotation through Context Analysis

Riad¹,

Elminir²,

Abdel-Ghany³

2013

IJAIS

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The goal of object level annotation is to locate and identify instances of an object category within an image. Nowadays, Most of the current object level annotation systems annotate the object according to the visual appearance in the image. Recognizing an object in an image based visual appearance yield ambiguity in object detection due to appearance confusion for example "sky" object may be annotated as "water" according to similarity in visual appearance. As a result, these systems don't recognize the objects in an image accurately due to the lack of scene context. In the task of visual object recognition, scene context can play important role in resolving the ambiguities in object detection. In order to solve the ambiguity problem, this paper presents a new technique for a context based object level annotation that considers both the semantic context and spatial context analysis to reduce ambiguous in object annotation.

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