Xiangyu Jin

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An on-line graphics recognition system is presented, which provides users a natural, convenient, and efficient way to input rigid and regular shapes or graphic objects (e.g., triangles, rectangles, ellipses, straight line, arrowheads, etc.) by quickly drawing their sketchy shapes in single or multiple strokes. An input sketchy (hand-drawn) shape is(More)
A novel and fast shape classification and regularization algorithm for on-line sketchy graphics recognition is proposed. We divided the on-line graphics recognition process into four stages: preprocessing, shape classification, shape fitting, and regularization. The Attraction Force Model is proposed to progressively combine the vertices on the input(More)
A spatial relation graph (SRG) and its partial matching method are proposed for online composite graphics representation and recognition. The SRG-based approach emphasizes three characteristics of on-line graphics recognition: partial, structural, and independent of stroke order and stroke number. A constrained partial permutation strategy is also proposed(More)
For small screen devices, such as PDAs, which totally depend on a pen-based user interface, traditional menu-selection/button-clicking based user interface becomes inconvenient for graphics inputting. In this paper, a novel sketch-based graphics inputting user interface is presented. By sketching a few constituent primitive shapes of the user-intended(More)
Conventional approaches to image retrieval are based on the assumption that relevant images are physically near the query image in some feature space. This is the basis of the cluster hypothesis. However, semantically related images are often scattered across several visual clusters. Although traditional Content-based Image Retrieval (CBIR) technologies may(More)
The views expressed here are those of the authors and do not necessarily represent those of NASA or the National Science Foundation. ABSTRACT Content-based image retrieval (CBIR) uses features that can be extracted from the images themselves. In previous work we have shown that using more than one representation of the images in a collection can improve the(More)
Our work in content-based image retrieval (CBIR) relies on content-analysis of multiple representations of an image which we term multiple viewpoints or channels. The conceptual idea is to place each image in multiple feature spaces and then perform retrieval by querying each of these spaces and merging the several responses. We have shown that a simple(More)
Many different communities have conducted research on the efficacy of relevance feedback in multimedia information systems. Unlike text IR, performance evaluation of multimedia IR systems tends to conform to the accepted standards of the community within which the work is conducted. This leads to idiosyncratic performance evaluations and hampers the ability(More)
Content-based image retrieval (CBIR) uses features that can be extracted from the images themselves. In previous work we have shown that using more than one representation of the images in a collection can improve the results presented to a user without changing the underlying feature extraction or search technologies[4]. In this paper we show that we can(More)