IEEE Trans Image Process - Self-adaptively Weighted Co-saliency Detection via Rank Constraint.

Tópicos

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Resumo

Co-saliency detection aims at discovering the common salient objects existing in multiple images. Most existing methods combine multiple saliency cues based on fixed weights, and ignore the intrinsic relationship of these cues. In this paper, we provide a general saliency map fusion framework, which exploits the relationship of multiple saliency cues and obtains the self-adaptive weight to generate the final saliency/cosaliency map. Given a group of images with similar objects, our method firstly utilizes several saliency detection algorithms to generate a group of saliency maps for all the images. The feature representation of the co-salient regions should be both similar and consistent. Therefore, the matrix jointing these feature histograms appears low rank. We formalize this general consistency criterion as the rank constraint, and propose two consistency energy to describe it, which are based on low rank matrix approximation and low rank matrix recovery, respectively. By calculating the self-adaptive weight based on the consistency energy, we highlight the common salient regions. Our method is valid for more than two input images and also works well for single image saliency detection. Experimental results on a variety of benchmark datasets demonstrate that the proposed method outperforms the state-of-the-art methods.

Resumo Limpo

cosali detect aim discov common salient object exist multipl imag exist method combin multipl salienc cue base fix weight ignor intrins relationship cue paper provid general salienc map fusion framework exploit relationship multipl salienc cue obtain selfadapt weight generat final saliencycosali map given group imag similar object method first util sever salienc detect algorithm generat group salienc map imag featur represent cosali region similar consist therefor matrix joint featur histogram appear low rank formal general consist criterion rank constraint propos two consist energi describ base low rank matrix approxim low rank matrix recoveri respect calcul selfadapt weight base consist energi highlight common salient region method valid two input imag also work well singl imag salienc detect experiment result varieti benchmark dataset demonstr propos method outperform stateoftheart method

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