IEEE Trans Image Process - A Probabilistic Associative Model for Segmenting Weakly-Supervised Images.

Tópicos

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Resumo

Weakly-supervised image segmentation is an important yet challenging task in image processing and pattern recognition fields. It is defined as: in the training stage, semantic labels are only at the image-level, without regard to their specific object/scene location within the image. Given a test image, the goal is to predict the semantics of every pixel/superpixel. In this paper, we propose a new weakly-supervised image segmentation model, focusing on learning the semantic associations between superpixel sets (graphlets in this work). In particular, we first extract graphlets from each image, where a graphlet is a small-sized graph measures the potential of multiple spatially neighboring superpixels (i.e., the probability of these superpixels sharing a common semantic label, such as the "sky" or the "sea"). To compare dierent-sized graphlets and to incorporate image-level labels, a manifold embedding algorithm is designed to transform all graphlets into equal-length feature vectors. Finally, we present a hierarchical Bayesian network (BN) to capture the semantic associations between post-embedding graphlets, based on which the semantics of each superpixel is inferred accordingly. Experimental results demonstrate that: 1) our approach performs competitively compared with the state-of-the-art approaches on three public data sets, and 2) considerable performance enhancement is achieved when using our approach on segmentation-based photo cropping and image categorization.

Resumo Limpo

weaklysupervis imag segment import yet challeng task imag process pattern recognit field defin train stage semant label imagelevel without regard specif objectscen locat within imag given test imag goal predict semant everi pixelsuperpixel paper propos new weaklysupervis imag segment model focus learn semant associ superpixel set graphlet work particular first extract graphlet imag graphlet smallsiz graph measur potenti multipl spatial neighbor superpixel ie probabl superpixel share common semant label sky sea compar dierents graphlet incorpor imagelevel label manifold embed algorithm design transform graphlet equallength featur vector final present hierarch bayesian network bn captur semant associ postembed graphlet base semant superpixel infer accord experiment result demonstr approach perform competit compar stateoftheart approach three public data set consider perform enhanc achiev use approach segmentationbas photo crop imag categor

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