Image browsing with PCA-assisted user-interaction

Keller, Ivo; Meiers, Thomas; Ellerbrock, Thomas M.; Sikora, Thomas

doi:10.1109/ivl.2001.990863

Cited by 15 publications

(4 citation statements)

References 12 publications

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“…Note, that in our interface ( [7]) the user is able to express his preference by adjusting only a few sliders. For that database, the user can align all images with respect to their shape, to their colour, or to their orientation.…”

Section: Resultsmentioning

confidence: 99%

“…Hence, the first challenging task is the dimension reduction of the feature space down to 2 or 3 dimensions of the visualization space. Several compression techniques have been applied: linear techniques as PCA [7] and Multi-Dimensional Scaling (MDS) [11] as well as nonlinear techniques like Neural Networks (NN) and Learning Vector Quantization (LVQ) [8]. In [19] a hierarchical selforganizing map (HSOM) is proposed, which uses the SOM algorithm to organize and visualize the images into a two-dimensional grid.…”

Section: Introductionmentioning

confidence: 99%

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<title>Relevance feedback as distribution generation</title>

2002

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Multimedia database interfaces should be designed to be very user-adaptive, since there is no generally applicable model of user's search behavior or of his search intention. First, the challenging task for the interface is to present the most representative objects in an appealing and concise manner. Second, the interface has to identify the user's search intention from very few positive feedbacks. In particular for the latter there exist a lot of Relevance Feedback implementations.While most of them are considered as more or less heuristically proved parameter adjustment procedures, we treat Relevance Feedback as direct probability density estimation. Our density is defined as the "hidden user's search intention" (HUSI), and -due to a lack of better criteria -it is derived directly from the feature space. Intuitively and positively associated objects influence a certain probability density by their characteristic, each for every dimension of its features. This density may be derived from a Bayesian approach and is regarded as the marginal distribution of the joint distribution presenting the HUSI. Then we generate samples, satisfying the HUSI, and we present them for a further iterative refinement. In addition, since the user can express his global preference by influencing the linear PCAcompression, we adjust the metrics as well as the HUSI.Our technique provides search results very close to psychological expectations.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

<title>Relevance feedback as distribution generation</title>

2002

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“…Mapping-based techniques employ dimensionality reduction techniques to map high-dimensional image feature vectors to a low-dimensional space for visualisation. Typical examples examples use principal component analysis (PCA) [10,11], multi-dimensional scaling (MDS) [12], or non-linear embedding techniques [13] to define a visualisation space onto which to place images. Clustering-based visualisations group visually similar images together, often in a hierarchical manner.…”

Section: Image Database Browsingmentioning

confidence: 99%

Intuitive Image Database Browsing to Support Image Annotation

Schaefer

Stuttard²

2012

Automatika

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Original scientific paperImage annotation is typically performed manually since automatic image annotation approaches have not matured yet to be used in practice. Consequently, image annotation is a labour intensive and time consuming task. In this paper, we show how an image browsing system can be employed to support efficient and effective (manual) annotation of image databases. In contrast to other approaches, which typically present images in a linear fashion, we employ a visualisation where images are arranged by mutual visual similarity. Since in this arrangement similar images are close to each other, they can easily be selected and annotated together. Organisation on a grid layout prevents image overlap and thus contributes to a clear presentation. Large image databases are handled through a hierarchical data structure where each image in the visualisation can correspond to a cluster of images that can be expanded by the user. Experimental results indicate that annotation can be performed faster on our proposed system. Key words:Visual information processing, Image databases, Image browsing, Image annotation, Hue sphere.Intuitivno pretraživanje baze slike kao potpora označavanju slika. Označavanje slika obično se obavlja ručno jer automatski pristupi još nisu dovoljno kvalitetni kako bi se koristili u praksi. Zbog toga je označavanje slika u bazi vremenski zahtjevno. U ovom radu pokazatćemo kako se sustav za pregled slika u bazi može koristiti kao učinkovita potpora ručnom označavanju slika. Za razliku od drugih pristupa, koji prikazuju slike u linearnom poretku, korištena je vizualizacija u kojoj su slike složene po medusobnoj sličnosti. Budući da su na taj način slične slike medusobno blizu jedna drugoj, lako ih je selektirati i zajednički označiti. Slike su organizirane u mrežni prikaz radi sprječavanja preklapanja i jasnije prezentacije. Velike baze podataka organizirane su u hijerarhijsku strukturu gdje svaka slika u pojedinoj vizualizaciji može pripadati skupu slikačiji prikaz korisnik po želji može proširivati. Rezultati provedenih eksperimenata pokazuju da se označavanje slika pomoću predloženog sustava može obavljati brže nego na uobičajeni način.Ključne riječi: obrada vizualnih informacija, baze slika, pretraživanje slika, označavanje slika

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“…Mapping-based techniques employ dimensionality reduction techniques to map highdimensional image feature vectors to a low-dimensional space for visualisation. Typical examples examples use principal component analysis (PCA) [7], [8], multi-dimensional scaling (MDS) [9], [10], or non-linear embedding techniques [11] to def ne a visualisation space onto which to place images. Clustering-based visualisations group visually similar images together, often in a hierarchical manner.…”

Section: Image Database Navigationmentioning

confidence: 99%

Mobile image browsing

Schaefer

2012

2012 16th IEEE Mediterranean Electrotechnical Conference

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Image collections, both private and commercial, are ever growing and therefore visual information is becoming more and more important. Moreover, the classical paradigm of how pictures are typically taken has changed completely, f rst with the spread of digital cameras and, more recently, with mobile devices equipped with integrated cameras.Clearly, these image repositories neeed to be managed, and tools for effectively and eff ciently searching image databases are highly sought after. In this paper, we present the idea of image database visualisation and image browsing as an intuitive and useful approach for accessing large image collections. After introducing the basic concepts, and some of our earlier (desktopbased) image browsing systems, we then focus on our latest work in this area which is the development of effective image browsing applications for mobile devices such as mobile phones and tablets.

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Image browsing with PCA-assisted user-interaction

Cited by 15 publications

References 12 publications

<title>Relevance feedback as distribution generation</title>

<title>Relevance feedback as distribution generation</title>

Intuitive Image Database Browsing to Support Image Annotation

Mobile image browsing

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