PATSI — Photo Annotation through Finding Similar Images with Multivariate Gaussian Models

Staněk, Michal; Broda, Bartosz; Kwaśnicka, Halina

doi:10.1007/978-3-642-15907-7_35

Cited by 5 publications

(5 citation statements)

References 13 publications

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“…Precision, recall and f-score quality measures are often chosen instead, e.g., [17][18][19][20][21][22][23]. Precision represents how well a classifier works, when it recognizes a class.…”

Section: Automatic Data Annotation and F-score Measurementioning

confidence: 99%

“…As the proposed approach has its roots in automatic data annotation, we should consider precision, recall and f-score as a quality measure [17][18][19][20][21][22][23]. Other typical features of automatic data annotation are large dictionaries and high-class imbalance.…”

Section: Automatic Data Annotation and F-score Measurementioning

confidence: 99%

“…The inspiration of the presented research is Photo Annotation Through Similar Images (PATSI) automatic image annotator [19][20][21]. We present it shortly for clarity, using the above formalism.…”

Section: Patsi-baseline Annotation Modelmentioning

confidence: 99%

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A new F-score gradient-based training rule for the linear model

Paradowski

Spytkowski

Kwaśnicka

2017

Pattern Anal Applic

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even more difficult, automatic data annotation problems are often considered as high-class imbalance problems. In this paper we address the basic recognition modelthe linear perceptron. On top of it, many other, more complex solutions may be proposed. The presented research is done from the perspective of automatic data annotation. 1.1 Linear recognition models Training of linear models has a long history. One shall note the classic Fisher's Linear Discriminant Analysis (LDA, e.g., [2]). Existence of closed-form, analytical solution is the largest advantage of discriminant analysis (both linear and quadratic). A disadvantage of linear discriminant analysis is the assumption on equality of covariance matrices for both classes. Also, it can cause typical difficulties related to zeroed or near-zeroed generalized variance [6] and covariance matrix inverse problems, especially for data with a large number of attributes. One of the possible solutions is to filter out attributes with zeroes-related eigenvalues [6]. Another possible solution is the use Regularized Discriminant Analysis (RDA) [3]. The basic assumption is that some recognition problems may be ill-posed due to insufficient amount of data comparing to the number of attributes. It combines together covariance matrix, diagonal variance matrix and identity matrix and thus makes the training process solvable. An interesting solution for LDA covariance matrix calculation is given by Fukunaga [4]. It combines both covariance matrices using weighted average instead of average, as originally proposed by Fisher. An extension of LDA is Kernel-LDA [5], which uses kernel trick known from Support Vector Machines to address linearly non-separable problems. The second family of approaches to train linear models is logistic regression (e.g., [6]). Logistic regression is Abstract Delta rule is a standard, well-established approach to train perceptron recognition model. However, mean squared error, on which it is based, is not suitable estimate for some problems, like information retrieval or automatic data annotation. F-score, a combination of precision and recall, is one of the major quality measures and can be used as an alternative. In this paper we present perceptron training model based on f-score. An approximate of f-score is proposed, based on components which are both continuous and differentiable. It allows to formulate a gradient-descent training routine, conceptually similar to the standard delta rule.

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“…Precision, recall and f-score quality measures are often chosen instead, e.g., [17][18][19][20][21][22][23]. Precision represents how well a classifier works, when it recognizes a class.…”

Section: Automatic Data Annotation and F-score Measurementioning

confidence: 99%

Section: Automatic Data Annotation and F-score Measurementioning

confidence: 99%

See 1 more Smart Citation

A new F-score gradient-based training rule for the linear model

Paradowski

Spytkowski

Kwaśnicka

2017

Pattern Anal Applic

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“…PATSI is build on the assumption that similar images share large parts of their annotations (Stanek et al 2010a, b). Such we essentially perform image retrieval by example with a subsequent annotation transfer step.…”

Section: Automatic Patsi With Variable Annotation Lengthmentioning

confidence: 99%

“…With precision and recall we use two well-known and often employed (Makadia et al 2008;Carneiro et al 2007;Nowak et al 2011;Nowak and Huiskes 2010;Stanek et al 2010a) measures of annotation relevance. Precision is the fraction of all retrieved words that are relevant, while recall is the fraction of relevant words that are retrieved.…”

Section: Evaluation Measuresmentioning

confidence: 99%

Image auto-annotation with automatic selection of the annotation length

2012

Self Cite

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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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Advances in Intelligent Image Analysis

Kwaśnicka

Jain

2011

Innovations in Intelligent Image Analysis

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PATSI — Photo Annotation through Finding Similar Images with Multivariate Gaussian Models

Cited by 5 publications

References 13 publications

A new F-score gradient-based training rule for the linear model

A new F-score gradient-based training rule for the linear model

Image auto-annotation with automatic selection of the annotation length

Advances in Intelligent Image Analysis

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