14516443
Learning a semantic space from user's relevance feedback for image retrieval
As current methods for content-based retrieval are incapable of capturing the semantics of images, we experiment with using spectral methods to infer a semantic space from user's relevance feedback, so that our system will gradually improve its retrieval performance through accumulated user interactions. In addition to the long-term learning process, we also model the traditional approaches to query refinement using relevance feedback as a short-term learning process. The proposed short- and long-term learning frameworks have been integrated into an image retrieval system. Experimental results on a large collection of images have shown the effectiveness and robustness of our proposed algorithms.
9650912
Learning an image manifold for retrieval
We consider the problem of learning a mapping function from low-level feature space to high-level semantic space. Under the assumption that the data lie on a submanifold embedded in a high dimensional Euclidean space, we propose a relevance feedback scheme which is naturally conducted only on the image manifold in question rather than the total ambient space. While images are typically represented by feature vectors in Rn, the natural distance is often different from the distance induced by the ambient space Rn. The geodesic distances on manifold are used to measure the similarities between images. However, when the number of data points is small, it is hard to discover the intrinsic manifold structure. Based on user interactions in a relevance feedback driven query-by-example system, the intrinsic similarities between images can be accurately estimated. We then develop an algorithmic framework to approximate the optimal mapping function by a Radial Basis Function (RBF) neural network. The semantics of a new image can be inferred by the RBF neural network. Experimental results show that our approach is effective in improving the performance of content-based image retrieval systems.
14585493
Learning a locality preserving subspace for visual recognition
We have demonstrated that the face recognition performance can be improved significantly in low dimensional linear subspaces. Conventionally, principal component analysis (PCA) and linear discriminant analysis (LDA) are considered effective in deriving such a face subspace. However, both of them effectively see only the Euclidean structure of face space. We propose a new approach to mapping face images into a subspace obtained by locality preserving projections (LPP) for face analysis. We call this Laplacianface approach. Different from PCA and LDA, LPP finds an embedding that preserves local information, and obtains a face space that best detects the essential manifold structure. In this way, the unwanted variations resulting from changes in lighting, facial expression, and pose may be eliminated or reduced. We compare the proposed Laplacianface approach with eigenface and fisherface methods on three test datasets. Experimental results show that the proposed Laplacianface approach provides a better representation and achieves lower error rates in face recognition.
1623769
Orthogonal locality preserving indexing
We consider the problem of document indexing and representation. Recently, Locality Preserving Indexing (LPI) was proposed for learning a compact document subspace. Different from Latent Semantic Indexing which is optimal in the sense of global Euclidean structure, LPI is optimal in the sense of local manifold structure. However, LPI is extremely sensitive to the number of dimensions. This makes it difficult to estimate the intrinsic dimensionality, while inaccurately estimated dimensionality would drastically degrade its performance. One reason leading to this problem is that LPI is non-orthogonal. Non-orthogonality distorts the metric structure of the document space. In this paper, we propose a new algorithm called Orthogonal LPI. Orthogonal LPI iteratively computes the mutually orthogonal basis functions which respect the local geometrical structure. Moreover, our empirical study shows that OLPI can have more locality preserving power than LPI. We compare the new algorithm to LSI and LPI. Extensive experimental results show that Orthogonal LPI obtains better performance than both LSI and LPI. More crucially, it is insensitive to the number of dimensions, which makes it an efficient data preprocessing method for text clustering, classification, retrieval, etc.
41188840
Finding Double Euler Trails of Planar Graphs in Linear Time
This paper answers an open question in the design of complimentary metal-oxide semiconductor VLSI circuits. The question asks whether a polynomial-time algorithm can decide if a given planar graph has a plane embedding ${\cal E}$ such that ${\cal E}$ has an Euler trail P = e1 e2 ... em and its dual graph has an Euler trail $P^*=e^*_1 e^*_2 \ldots e^*_m$, where $e^*_i$ is the dual edge of ei for i=1,2,...,m. This paper answers this question in the affirmative by presenting a linear-time algorithm.
949912
Replicated Shared Object Model for Edge Detection with Spiral Architecture
Edge detection in computer vision and image processing is a process which detects one kind of significant features appearing as discontinuities in intensities. A parallel edge detection algorithm based on Spiral Architeture is designed in this paper by a Replicated Shared Object Model (RSOM). We use this approach to meet the need of replicated object management, update propagation, underlying communication and consistency maintenance among replicated objects.
1711864
Automatically Generating Gene Summaries from Biomedical Literature
Biologists often need to find information about genes whose function is not described in the genome databases. Currently they must try to search disparate biomedical literature to locate relevant articles, and spend considerable efforts reading the retrieved articles in order to locate the most relevant knowledge about the gene. We describe our software, the first that automatically generates gene summaries from biomedical literature. We present a two-stage summarization method, which involves first retrieving relevant articles and then extracting the most informative sentences from the retrieved articles to generate a structured gene summary. The generated summary explicitly covers multiple aspects of a gene, such as the sequence information, mutant phenotypes, and molecular interaction with other genes. We propose several heuristic approaches to improve the accuracy in both stages. The proposed methods are evaluated using 10 randomly chosen genes from FlyBase and a subset of Medline abstracts about Drosophila. The results show that the precision of the top selected sentences in the 6 aspects is typically about 50-70%, and the generated summaries are quite informative, indicating that our approaches are effective in automatically summarizing literature information about genes. The generated summaries not only are directly useful to biologists but also serve as useful entry points to enable them to quickly digest the retrieved literature articles.
701
Common-Face Embeddings of Planar Graphs
Given a planar graph $\Ggg$ and a sequence ${\CC}_1,\ldots,{\CC}_q$, where each ${\CC}_i$ is a family of vertex subsets of $\Ggg$, we wish to find a plane embedding of $\Ggg$, if any exists, such that, for each $i\in\{1,\ldots,q\}$, there is a face Fi in the embedding whose boundary contains at least one vertex from each set in CCi. This problem has applications in the recovery of topological information from geographical data and the design of constrained layouts in VLSI. Let $\inputsize$ be the input size, i.e., the total number of vertices and edges in $\Ggg$ and the families CCi, counting multiplicity. We show that this problem is NP-complete in general. We also show that it is solvable in $O(\inputsize\log \inputsize)$ time for the special case in which, for each input family CCi, each set in CCi induces a connected subgraph of the input graph $\Ggg$. Note that the classical problem of simply finding a planar embedding is a further special case of this case with q=0. Therefore, the processing of the additional constraints CC1, . . .,CCq incurs only a logarithmic factor of overhead.
20141160
A method for analyzing properties of hierarchical predicate transition nets
Hierarchical high level Petri nets have been proposed in recent years as a powerful formal method for modeling large concurrent and distributed systems but they are even more difficult to analyze than flat high level Petri nets, which are also lacking effective analysis methods themselves. A method for analyzing properties of hierarchical predicate transition Petri nets is proposed. The method employs two temporal induction techniques, one for safety properties and the other for liveness properties, without using temporal logic formalism. Within each induction technique, a hybrid reasoning technique combining net structural and behavioral reasoning, and ordinary first order logic reasoning is used.