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This paper introduces a web image search reranking approach that explores multiple modalities in a graph-based learning scheme. Different from the conventional methods that usually adopt a single modality or integrate multiple modalities into a long feature vector, our approach can effectively integrate the learning of relevance scores, weights of(More)
—Weakly-supervised image segmentation is a challenging problem with multidisciplinary applications in multimedia content analysis and beyond. It aims to segment an image by lever-aging its image-level semantics (i.e., tags). This paper presents a weakly-supervised image segmentation algorithm that learns the distribution of spatially structural superpixel(More)
Extracting discriminative and robust features from video sequences is the first and most critical step in human action recognition. In this paper, instead of using handcrafted features, we automatically learn spatio-temporal motion features for action recognition. This is achieved via an evolutionary method, i.e., genetic programming (GP), which evolves the(More)
3D object retrieval has attracted extensive research efforts and become an important task in recent years. It is noted that how to measure the relevance between 3D objects is still a difficult issue. Most of the existing methods employ just the model-based or view-based approaches, which may lead to incomplete information for 3D object representation. In(More)
Manifold learning have attracted considerable attention over the last decade. The most frequently used functional is the l 2-norm of the gradient of the function. In this paper, we consider the linear manifold learning problem by minimizing this functional with appropriate constraint. We provide theoretical analysis on both the functional and the(More)
In view-based 3D object retrieval and recognition, each object is described by multiple views. A central problem is how to estimate the distance between two objects. Most conventional methods integrate the distances of view pairs across two objects as an estimation of their distance. In this paper, we propose a discriminative probabilistic object modeling(More)