Analysis of Inter-rater Agreement among Human Observers Who Judge Image Similarity

Michalak, Krzysztof; Dzieńkowski, Bartłomiej Józef; Hudyma, Elżbieta; Staněk, Michal

doi:10.1007/978-3-642-20320-6_26

Cited by 1 publication

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“…This formulation leads directly to the main challenge that automatic image annotation, image retrieval, possibly the whole field of computer vision (according to Tousch et al 2012) faces: the semantic gap. Defined by Smeulders et al (2000) as the 'lack of coincidence between the information that one can extract from the visual data and the interpretation that the same data has for a user in a given situation', the semantic gap names the struggle to model the barely understood human visual perception 1 (Michalak et al 2011). The task is further complicated by the high subjectivity of an annotations relevance.…”

Section: Automatic Patsi With Variable Annotation Lengthmentioning

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: Automatic Patsi With Variable Annotation Lengthmentioning

confidence: 99%