1733796
Event detection from evolution of click-through data
Previous efforts on event detection from the web have focused primarily on web content and structure data ignoring the rich collection of web log data. In this paper, we propose the first approach to detect events from the click-through data, which is the log data of web search engines. The intuition behind event detection from click-through data is that such data is often event-driven and each event can be represented as a set ofquery-page pairs that are not only semantically similar but also have similar evolution pattern over time. Given the click-through data, in our proposed approach, we first segment it into a sequence of bipartite graphs based on theuser-defined time granularity. Next, the sequence of bipartite graphs is represented as a vector-based graph, which records the semantic and evolutionary relationships between queries and pages. After that, the vector-based graph is transformed into its dual graph, where each node is a query-page pair that will be used to represent real world events. Then, the problem of event detection is equivalent to the problem of clustering the dual graph of the vector-based graph. The clustering process is based on a two-phase graph cut algorithm. In the first phase, query-page pairs are clustered based on thesemantic-based similarity such that each cluster in the result corresponds to a specific topic. In the second phase, query-page pairs related to the same topic are further clustered based on the evolution pattern-based similarity such that each cluster is expected to represent a specific event under the specific topic. Experiments with real click-through data collected from a commercial web search engine show that the proposed approach produces high quality results.
18011307
Subspace Clustering and Label Propagation for Active Feedback in Image Retrieval
In recent years, relevance feedback has been studied extensively as a way to improve performance of content-based image retrieval (CBIR). However, since users are usually unwilling to provide many feedbacks, the insufficiency of the training samples limited the success of relevance feedback. To tackle this problem, we propose two coupled algorithms: (i) overlapped subspace clustering to select representative images for user’s feedback; and (ii) multi-subspace label propagation to include unlabeled data in the training process. As these two algorithms are both working on sub feature spaces of the image database, they can not only deal with the insufficient training samples but also well capture the user’s attention during the retrieval process. Experimental results on a large database of general-purposed images demonstrated the high effectiveness of our proposed algorithms.
10695800
Star-Structured High-Order Heterogeneous Data Co-clustering Based on Consistent Information Theory
Heterogeneous object co-clustering has become an important research topic in data mining. In early years of this research, people mainly worked on two types of heterogeneous data (denoted by pair-wise co-clustering); while recently more and more attention was paid to multiple types of heterogeneous data (denoted by high- order co-clustering). In this paper, we studied the high- order co-clustering of objects with star-structured interrelationship, i.e., there is a central type of objects that connects the other types of objects. Actually, this case could be a very good model for many real-world applications, such as the co-clustering of Web images, their low-level visual features, and the surrounding text. We used a tripartite graph to represent the interrelationships among different objects, and proposed a consistent information theory which generates an effective algorithm to obtain the co-clusters of different types of objects. Experiments on a Web image show that our proposed algorithm is a better choice compared with previous work on heterogeneous object co-clustering.
15516376
Supervised rank aggregation
This paper is concerned with rank aggregation, the task of combining the ranking results of individual rankers at meta-search. Previously, rank aggregation was performed mainly by means of unsupervised learning. To further enhance ranking accuracies, we propose employing supervised learning to perform the task, using labeled data. We refer to the approach as Supervised Rank Aggregation. We set up a general framework for conducting Supervised Rank Aggregation, in which learning is formalized an optimization which minimizes disagreements between ranking results and the labeled data. As case study, we focus on Markov Chain based rank aggregation in this paper. The optimization for Markov Chain based methods is not a convex optimization problem, however, and thus is hard to solve. We prove that we can transform the optimization problem into that of Semidefinite Programming and solve it efficiently. Experimental results on meta-searches show that Supervised Rank Aggregation can significantly outperform existing unsupervised methods.
12919975
Image Recognition with Occlusions
We study the problem of how to detect “interesting objects” appeared in a given image, I. Our approach is to treat it as a function approximation problem based on an over-redundant basis. Since the basis (a library of image templates) is over-redundant, there are infinitely many ways to decompose I. To select the “best” decomposition we first propose a global optimization procedure that considers a concave cost function derived from a “weighted L p norm” with 0<p<-1. This concave cost function selects as few coefficients as possible producing a sparse representation of the image and handle occlusions. However, it contains multiple local minima. We identify all local minima so that a global optimization is possible by visiting all of them. Secondly, because the number of local minima grows exponentially with the number of templates, we investigate a greedy “L p Matching Pursuit” strategy.
52838645
Representation and Self-Similarity of Shapes
Representing shapes in a compact and informative form is a significant problem for vision systems that must recognize or classify objects. We describe a compact representation model for two-dimensional (2D) shapes by investigating their self-similarities and constructing their shape axis trees (SA-trees). Our approach can be formulated as a variational one (or, equivalently, as MAP estimation of a Markov random field). We start with a 2D shape, its boundary contour, and two different parameterizations for the contour (one parameterization is oriented counterclockwise and the other clockwise). To measure its self-similarity, the two parameterizations are matched to derive the best set of one-to-one point-to-point correspondences along the contour. The cost functional used in the matching may vary and is determined by the adopted self-similarity criteria, e.g., cocircularity, distance variation, parallelism, and region homogeneity. The loci of middle points of the pairing contour points yield the shape axis and they can be grouped into a unique free tree structure, the SA-tree. By implicitly encoding the (local and global) shape information into an SA-tree, a variety of vision tasks, e.g., shape recognition, comparison, and retrieval, can be performed in a more robust and efficient way via various tree-based algorithms. A dynamic programming algorithm gives the optimal solution in O(N/sup 1/), where N is the size of the contour.
2588176
Local discriminant embedding and its variants
We present a new approach, called local discriminant embedding (LDE), to manifold learning and pattern classification. In our framework, the neighbor and class relations of data are used to construct the embedding for classification problems. The proposed algorithm learns the embedding for the submanifold of each class by solving an optimization problem. After being embedded into a low-dimensional subspace, data points of the same class maintain their intrinsic neighbor relations, whereas neighboring points of different classes no longer stick to one another. Via embedding, new test data are thus more reliably classified by the nearest neighbor rule, owing to the locally discriminating nature. We also describe two useful variants: two-dimensional LDE and kernel LDE. Comprehensive comparisons and extensive experiments on face recognition are included to demonstrate the effectiveness of our method.
24769601
Recognizing articulated objects with information theoretic methods
This paper addresses the problem of recognizing articulated and deformable objects. In particular we are interested in human arm and leg articulations. Our approach is a Bayesian-Information integration of shape similarity and snakes, and naturally combines top-down and bottom-up algorithms. The bottom-up method extracts edges, then constructs snakes (or contours) by grouping edge elements and feeds the shape analysis. The top-down one uses shape analysis, by comparing the object model with the extracted snakes, to guide/prune the search for other snakes. The optimizations are based on Dijkstra algorithm and further pruning of this algorithm is obtained by "integration by parts". Our approach is general enough to handle three dimensional objects, but our focus here is on two dimensional contours.
6716050
Semantic manifold learning for image retrieval
Learning the user's semantics for CBIR involves two different sources of information: the similarity relations entailed by the content-based features, and the relevance relations specified in the feedback. Given that, we propose an augmented relation embedding (ARE) to map the image space into a semantic manifold that faithfully grasps the user's preferences. Besides ARE, we also look into the issues of selecting a good feature set for improving the retrieval performance. With these two aspects of efforts we have established a system that yields far better results than those previously reported. Overall, our approach can be characterized by three key properties: 1) The framework uses one relational graph to describe the similarity relations, and the other two to encode the relevant/irrelevant relations indicated in the feedback. 2) With the relational graphs so defined, learning a semantic manifold can be transformed into solving a constrained optimization problem, and is reduced to the ARE algorithm accounting for both the representation and the classification points of views. 3) An image representation based on augmented features is introduced to couple with the ARE learning. The use of these features is significant in capturing the semantics concerning different scales of image regions. We conclude with experimental results and comparisons to demonstrate the effectiveness of our method.