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Multi-Label Sparse Coding for Automatic Image Annotation

Multi-Label Sparse Coding for Automatic Image Annotation

Multi-Label Sparse Coding for Automatic Image

Multi-Label Sparse Coding for Automatic Image AnnotationChanghu Wang 1∗ , Shuicheng Yan 2 , Lei Zhang 3 , Hong-Jiang Zhang 41 MOE-MS Key Lab of MCC, University of Science and Technology of China2 Department of Electrical and Computer Engineering, National University of Singapore3 Microsoft Research Asia, 4 Microsoft Advanced Technology Center, Beijing, Chinawch@ustc.edu, eleyans@nus.edu.sg, {leizhang,hjzhang}@microsoft.comAbstractIn this paper, we present a multi-label sparse codingframework for feature extraction and classification withinthe context of automatic image annotation. First, each imageis encoded into a so-called supervector, derived fromthe universal Gaussian Mixture Models on orderless imagepatches. Then, a label sparse coding based subspacelearning algorithm is derived to effectively harness multilabelinformation for dimensionality reduction. Finally, thesparse coding method for multi-label data is proposed topropagate the multi-labels of the training images to thequery image with the sparse l 1 reconstruction coefficients.Extensive image annotation experiments on the Corel5k andCorel30k databases both show the superior performance ofthe proposed multi-label sparse coding framework over thestate-of-the-art algorithms.1. IntroductionAutomatic image annotation, whose goal is to automaticallyassign the images with the keywords, has been anactive research topic owing to its great potentials in imageretrieval and management systems. Image annotation is essentiallya typical multi-label learning problem, where eachimage could contain multiple objects and therefore could beassociated with a set of labels. Since generally it is tediousand time-consuming for humans to manually annotate thekeywords in the object/region level for data collection, insteadthe keywords are usually labeled in the image level,which makes the automatic image annotation problem evenmore challenging.The image annotation problem has been extensivelystudied in recent years. The popular algorithms can beroughly divided into three categories: classification-based∗ Changhu Wang performed this work while being a Research Engineerat the Department of Electrical and Computer Engineering, National Universityof Singapore.methods, probabilistic modeling-based methods, and Webimage related methods. The classification-based methods[3][5][6][16] use image classifiers to represent annotationkeywords (concepts). The probabilistic modeling-basedmethods [2][9][10][11][14][17] attempt to infer the correlationsor joint probabilities between images and annotationkeywords. Web image related methods [20][21][22][23] tryto solve image annotation problem in Web environment.There are also some attempts to use multi-label learningalgorithms to solve image annotation problem, which arescattered in different categories mentioned above. Most ofexisting attempts of using multi-label learning algorithms[13][26] to solve image annotation problem mainly focuson mining the label relationship for better annotation performance.In spite of these many algorithms proposed with differentmotivations, the underlying question, i.e. howtoeffectivelymeasure the semantic similarity between two imageswith multiple objects/semantics, is still not well answered.There are mainly three kinds of features for imagerepresentation, i.e. global features [20][23], region-basedfeatures [9][11][14], and patch-based features (or local descriptors)[3][10], out of which the region-based featuresseem the most reasonable for the above-mentioned multilabelessence of images. However, in practice, on the onehand, it is too time-consuming to manually segment imagesinto regions; on the other hand, without human interaction,the automatic image segmentation algorithms arefar from satisfaction. Thus, the existing works based onregion-based features [9][11][14] are often inferior to thethose patch-based algorithms [3][10].An inevitable and practical choice for image annotationis then to use global features or patch-based features insteadof region-based features. In most existing algorithms,the global features or patch-based features are directly comparedto determine the image-to-image similarity. However,there are usually multiple semantic concepts in one image,and two images containing one same object may have additionaldifferent objects too. For example, as shown in Fig.1978-1-4244-3991-1/09/$25.00 ©2009 IEEE1643

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