Image retrieval and perceptual similarity

Neumann, Dirk; Gegenfurtner, Karl R.

doi:10.1145/1119766.1119769

Cited by 48 publications

(38 citation statements)

References 35 publications

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“…However, even with the apparent limitations of concept-based image retrieval, such as the cost (Layne, 1994) and the variable precision of manual annotations, contentbased image retrieval systems have failed thus far to bridge the semantic gap between indexer and end user (Datta, Li, & Wang, 2005;Enser, 2008;Lew, Sebe, Djeraba, & Jain, 2006;Neumann & Gegenfurtner, 2006;Tjondronegoro et al, 2009). Because words are the only means for describing the semantic content of images, at least for the foreseeable future, image retrieval systems will depend on text annotations, especially for image collections on social networking and photo-sharing services such as Flickr where users add textual tags and descriptions to images.…”

Section: Introductionmentioning

confidence: 99%

A comparative study of Flickr tags and index terms in a general image collection

Rorissa

2010

J. Am. Soc. Inf. Sci.

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Web 2.0 and social/collaborative tagging have altered the traditional roles of indexer and user. Traditional indexing tools and systems assume the top-down approach to indexing in which a trained professional is responsible for assigning index terms to information sources with a potential user in mind. However, in today's Web, end users create, organize, index, and search for images and other information sources through social tagging and other collaborative activities. One of the impediments to user-centered indexing had been the cost of soliciting user-generated index terms or tags. Social tagging of images such as those on Flickr, an online photo management and sharing application, presents an opportunity that can be seized by designers of indexing tools and systems to bridge the semantic gap between indexer terms and user vocabularies. Empirical research on the differences and similarities between user-generated tags and index terms based on controlled vocabularies has the potential to inform future design of image indexing tools and systems.Toward this end, a random sample of Flickr images and the tags assigned to them were content analyzed and compared with another sample of index terms from a general image collection using established frameworks for image attributes and contents. The results show that there is a fundamental difference between the types of tags and types of index terms used. In light of this, implications for research into and design of user-centered image indexing tools and systems are discussed.

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Section: Introductionmentioning

confidence: 99%

A comparative study of Flickr tags and index terms in a general image collection

Rorissa

2010

J. Am. Soc. Inf. Sci.

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“…Estimated visual similarity has been shown to correspond well to subjectively perceived similarity [1]. Thus, initially, we did not additionally validate the similarity by having human observers make judgements between images from different categories.…”

Section: Methodsmentioning

confidence: 99%

Conceptual and Visual Features Contribute to Visual Memory for Natural Images

Huebner

Gegenfurtner

2012

PLoS ONE

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We examined the role of conceptual and visual similarity in a memory task for natural images. The important novelty of our approach was that visual similarity was determined using an algorithm [1] instead of being judged subjectively. This similarity index takes colours and spatial frequencies into account. For each target, four distractors were selected that were (1) conceptually and visually similar, (2) only conceptually similar, (3) only visually similar, or (4) neither conceptually nor visually similar to the target image. Participants viewed 219 images with the instruction to memorize them. Memory for a subset of these images was tested subsequently. In Experiment 1, participants performed a two-alternative forced choice recognition task and in Experiment 2, a yes/no-recognition task. In Experiment 3, testing occurred after a delay of one week. We analyzed the distribution of errors depending on distractor type. Performance was lowest when the distractor image was conceptually and visually similar to the target image, indicating that both factors matter in such a memory task. After delayed testing, these differences disappeared. Overall performance was high, indicating a large-capacity, detailed visual long-term memory.

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“…For example, Neumann and Gegenfurtner [7] studied how perceptual similarity factored into image retrieval algorithm and system design. Sanchez et al [8] studied the modeling of subjectivity in the visual perception of orientation in image retrieval.…”

Section: Related Workmentioning

confidence: 99%

A novel graphical user interface for high-efficacy modeling of human perceptual similarity opinions

Kress

Tourassi

2013

Medical Imaging 2013: Image Perception, Observer Performance, and Technology Assessment

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We present a novel graphical user interface (GUI) that facilitates high-efficacy collection of perceptual similarity opinions of a user in an effective and intuitive manner. The GUI is based on a hybrid mechanism that combines ranking and rating. Namely, it presents a base image for rating its similarity to seven peripheral images that are simultaneously displayed in a circular layout. The user is asked to report the base image's pairwise similarity to each peripheral image on a fixed scale while preserving the relative ranking among all peripheral images. The collected data are then used to predict the user's subjective opinions regarding the perceptual similarity of images. We tested this new approach against two methods commonly used in perceptual similarity studies: (1) a ranking method that presents triplets of images for selecting the image pair with the highest internal similarity and (2) a rating method that presents pairs of images for rating their relative similarity on a fixed scale. We aimed to determine which data collection method was the most time efficient and effective for predicting a user's perceptual opinions regarding the similarity of mammographic masses. Our study was conducted with eight individuals. By using the proposed GUI, we were able to derive individual perceptual similarity profiles with a prediction accuracy ranging from 76.83% to 92.06% which was 41.4% to 46.9% more accurate than those derived with the other two data collection GUIs. The accuracy improvement was statistically significant.

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Image retrieval and perceptual similarity

Cited by 48 publications

References 35 publications

A comparative study of Flickr tags and index terms in a general image collection

A comparative study of Flickr tags and index terms in a general image collection

Conceptual and Visual Features Contribute to Visual Memory for Natural Images

A novel graphical user interface for high-efficacy modeling of human perceptual similarity opinions

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