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Developing effective fusion schemes for multiple feature types has always been a hot issue in content-based image retrieval. In this paper, we propose a novel method for graph based visual reranking, which addresses two major limitations in existing methods. Firstly, in the phase of graph construction, our method introduces fine-grained measurements for image relations, by assigning the edge weights...
The recent decade has witnessed remarkable developments of SIFT-based approaches for image retrieval. However, such approaches are inherently insufficient in handling the semantic gap and large viewpoint changes, leading to inferior performance. To address these limitations, this paper extends SIFT-based match kernels by integrating the match functions for SIFT and CNN features. Specifically, a thresholded...
Many visual applications have benefited from the outburst of web images, yet the imprecise and incomplete tags arbitrarily provided by users, as the thorn of the rose, may hamper the performance of retrieval or indexing systems relying on such data. In this paper, we propose a novel locality sensitive low-rank model for image tag completion, which approximates the global nonlinear model with a collection...
One of the fundamental problems in image search is to learn the ranking functions, i.e., similarity between the textual query and visual image. A number of research paradigms, ranging from feature-based vector model to image ranker learning, have been applied to measure query-image similarity. However, most of the existing similarity learning methods either depend on surrounding texts for ranking...
Recognising and understanding the activities performed by people is a fundamental research topic in developing a wi de range of applications that would be societally beneficial. In this article, we present and discuss two research projects on human action recognition based on computer vision techniques. We also report an ongoing research project that focuses on learning human activities through low...
This paper proposes a general method for size optimization in dense sampling to obtain a better representation of an image. Our method can be utilized to improve the performance of image classification and other tasks. We discuss the spatial consistency in global-scope restrained descriptors, by analyzing the appropriate sampling size. We apply the low rank method to solve the representative matrix...
Modern clothes are designed based on various visual elements of different fashion styles. Traditional vision-based clothes recommendation methods focused on searching clothes which are similar with user preferred samples in the aspects of colors and partial shape elements. In this paper, we propose a method of recommending clothes by mining visual elements of different fashion styles. Independent...
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