IEEE Trans Image Process - Image annotation by input-output structural grouping sparsity.

Tópicos

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Resumo

Automatic image annotation (AIA) is very important to image retrieval and image understanding. Two key issues in AIA are explored in detail in this paper, i.e., structured visual feature selection and the implementation of hierarchical correlated structures among multiple tags to boost the performance of image annotation. This paper simultaneously introduces an input and output structural grouping sparsity into a regularized regression model for image annotation. For input high-dimensional heterogeneous features such as color, texture, and shape, different kinds (groups) of features have different intrinsic discriminative power for the recognition of certain concepts. The proposed structured feature selection by structural grouping sparsity can be used not only to select group-of-features but also to conduct within-group selection. Hierarchical correlations among output labels are well represented by a tree structure, and therefore, the proposed tree-structured grouping sparsity can be used to boost the performance of multitag image annotation. In order to efficiently solve the proposed regression model, we relax the solving process as a framework of the bilayer regression model for multilabel boosting by the selection of heterogeneous features with structural grouping sparsity (Bi-MtBGS). The first-layer regression is to select the discriminative features for each label. The aim of the second-layer regression is to refine the feature selection model learned from the first layer, which can be taken as a multilabel boosting process. Extensive experiments on public benchmark image data sets and real-world image data sets demonstrate that the proposed approach has better performance of multitag image annotation and leads to a quite interpretable model for image understanding.

Resumo Limpo

automat imag annot aia import imag retriev imag understand two key issu aia explor detail paper ie structur visual featur select implement hierarch correl structur among multipl tag boost perform imag annot paper simultan introduc input output structur group sparsiti regular regress model imag annot input highdimension heterogen featur color textur shape differ kind group featur differ intrins discrimin power recognit certain concept propos structur featur select structur group sparsiti can use select groupoffeatur also conduct withingroup select hierarch correl among output label well repres tree structur therefor propos treestructur group sparsiti can use boost perform multitag imag annot order effici solv propos regress model relax solv process framework bilay regress model multilabel boost select heterogen featur structur group sparsiti bimtbg firstlay regress select discrimin featur label aim secondlay regress refin featur select model learn first layer can taken multilabel boost process extens experi public benchmark imag data set realworld imag data set demonstr propos approach better perform multitag imag annot lead quit interpret model imag understand

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