13625798
Data-driven approach for bridging the cognitive gap in image retrieval
Bridging the cognitive gap in image retrieval has been an active research direction in recent years. Existing solutions typically require a large volume of training data that could be difficult to obtain in practice. In this paper, we propose a data-driven approach that uses Web images and their surrounding textual annotations as the source of training data to bridge the cognitive gap. We construct an image thesaurus that contains a set of codewords, each representing a semantically related subspace in the feature space. We also explore the use of query expansion based on the constructed image thesaurus for improving image retrieval performance.
15083897
Grouping web image search result
In this paper, we propose a Web image search result organizing method to facilitate user browsing. We formalize this problem as a salient image region pattern extraction problem. Given the images returned by Web search engine, we first segment the images into homogeneous regions and quantize the environmental regions into image codewords. The salient codeword "phrases" are then extracted and ranked based on a regression model learned from human labeled training data. According to the salient "phrases", images are assigned to different clusters, with the one nearest to the centroid as the entry for the corresponding cluster. Satisfying experimental results show the effectiveness of our proposed method.
15578823
Exploring statistical correlations for image retrieval
Bridging the cognitive gap in image retrieval has been an active research direction in recent years, of which a key challenge is to get enough training data to learn the mapping functions from low-level feature spaces to high-level semantics. In this paper, image regions are classified into two types: key regions representing the main semantic contents and environmental regions representing the contexts. We attempt to leverage the correlations between types of regions to improve the performance of image retrieval. A Context Expansion approach is explored to take advantages of such correlations by expanding the key regions of the queries using highly correlated environmental regions according to an image thesaurus. The thesaurus serves as both a mapping function between image low-level features and concepts and a store of the statistical correlations between different concepts. It is constructed through a data-driven approach which uses Web data (images, their surrounding textual annotations) as training data source to learn the region concepts and to explore the statistical correlations. Experimental results on a database of 10,000 general-purpose images show the effectiveness of our proposed approach in both improving search precision (i.e. filter irrelevant images) and recall (i.e. retrieval relevant images whose context may be varied). Several major factors which have impact on the performance of our approach are also studied.
579452
Multi-model similarity propagation and its application for web image retrieval
In this paper, we propose an iterative similarity propagation approach to explore the inter-relationships between Web images and their textual annotations for image retrieval. By considering Web images as one type of objects, their surrounding texts as another type, and constructing the links structure between them via webpage analysis, we can iteratively reinforce the similarities between images. The basic idea is that if two objects of the same type are both related to one object of another type, these two objects are similar; likewise, if two objects of the same type are related to two different, but similar objects of another type, then to some extent, these two objects are also similar. The goal of our method is to fully exploit the mutual reinforcement between images and their textual annotations. Our experiments based on 10,628 images crawled from the Web show that our proposed approach can significantly improve Web image retrieval performance.
14488825
Image annotation using search and mining technologies
In this paper, we present a novel solution to the image annotation problem which annotates images using search and data mining technologies. An accurate keyword is required to initialize this process, and then leveraging a large-scale image database, it 1) searches for semantically and visually similar images, 2) and mines annotations from them. A notable advantage of this approach is that it enables unlimited vocabulary, while it is not possible for all existing approaches. Experimental results on real web images show the effectiveness and efficiency of the proposed algorithm.
3114897
Multi-graph enabled active learning for multimodal web image retrieval
In this paper, we propose a multimodal Web image retrieval technique based on multi-graph enabled active learning. The main goal is to leverage the heterogeneous data on the Web to improve retrieval precision. Three graphs are constructed on images' content features, textual annotations and hyperlinks respectively, namely Content-Graph, Text-Graph and Link-Graph, which provide complimentary information on the images. By analyzing the three graphs, a training dataset is automatically created and transductive learning is enabled. The transductive learner is a multi-graph based classifier, which simultaneously solves the learning problem and the problem of combining heterogeneous data. This proposed approach, overall, tackles the problem of unsupervised active learning on Web graph. Although the proposed approach is discussed in the context of WWW image retrieval, it can be applied to other domains. The experimental results show the effectiveness of our approach.
15978047
The dynamical stability of reverberatory neural circuits
Abstract. The concept of reverberation proposed by Lorente de Nó and Hebb is key to understanding strongly recurrent cortical networks. In particular, synaptic reverberation is now viewed as a likely mechanism for the active maintenance of working memory in the prefrontal cortex. Theoretically, this has spurred a debate as to how such a potentially explosive mechanism can provide stable working-memory function given the synaptic and cellular mechanisms at play in the cerebral cortex. We present here new evidence for the participation of NMDA receptors in the stabilization of persistent delay activity in a biophysical network model of conductance-based neurons. We show that the stability of working-memory function, and the required NMDA/AMPA ratio at recurrent excitatory synapses, depend on physiological properties of neurons and synaptic interactions, such as the time constants of excitation and inhibition, mutual inhibition between interneurons, differential NMDA receptor participation at excitatory projections to pyramidal neurons and interneurons, or the presence of slow intrinsic ion currents in pyramidal neurons. We review other mechanisms proposed to enhance the dynamical stability of synaptically generated attractor states of a reverberatory circuit. This recent work represents a necessary and significant step towards testing attractor network models by cortical electrophysiology.
16978334
Evaluating a class of distance-mapping algorithms for data mining and clustering
A distance-mapping algorithm takes a set of objects and a distance metric and then maps those objects to a Euclidean or pseudoEuclidean space in such a way that the distances among objects are approximately preserved. Distance mapping algorithms are a useful tool for clustering and visualization in data intensive applications, because they replace expensive distance calculations by sum-of-square calculations. This can make clustering in large databases with expensive distance metrics practical. In this paper we present five distance-mapping algorithms and conduct experiments to compare their performance in data clustering applications. These include two algorithms called FastMap and MetricMap, and three hybrid heuristics that combine the two algorithms in different ways. Experimental results on both synthetic and RNA data show the superiority of the hybrid algorithms. The results imply that FastMap and MetricMap capture complementary information about distance metrics and therefore can be used together to great benefit. The net effect is that multi-day computations may be done in minutes.
10922426
Language Model Information Retrieval with Document Expansion
Language model information retrieval depends on accurate estimation of document models. In this paper, we propose a document expansion technique to deal with the problem of insufficient sampling of documents. We construct a probabilistic neighborhood for each document, and expand the document with its neighborhood information. The expanded document provides a more accurate estimation of the document model, thus improves retrieval accuracy. Moreover, since document expansion and pseudo feedback exploit different corpus structures, they can be combined to further improve performance. The experiment results on several different data sets demonstrate the effectiveness of the proposed document expansion method.
612178
A learning-based approach to estimate statistics of operators in continuous queries: a case study
Statistic estimation such as output size estimation of operators is a well-studied subject in the database research community, mainly for the purpose of query optimization. The assumption, however, is that queries are ad-hoc and therefore the emphasis has been on capturing the data distribution. When long standing continuous queries on a changing database are concerned, a more direct approach, namely building an estimation model for each operator, is possible. In this paper, we propose a novel learning-based method. Our method consists of two steps. The first step is to design a dedicated feature extraction algorithm that can be used incrementally to obtain feature values from the underlying data. The second step is to use a data mining algorithm to generate an estimation model based on the feature values extracted from the historical data. To illustrate the approach, this paper studies the case of similarity-based searches over streaming time series. Experimental results show this approach provides accurate statistic estimates with a low overhead.
7760234
Automated Discovery of Active Motifs in Three Dimensional Molecules
In this paper we present a method for discovering approximately common motifs (also known as active motifs) in three dimensional (3D) molecules. Each node in a molecule is represented by a 3D point in the Euclidean Space and each edge is represented by an undirected line segment connecting two nodes in the molecule. Motifs are rigid substructures which may occur in a molecule after allowing for an arbitrary number of rotations and translations as well as a small number (specified by the user) of node insert/delete operations in the motifs or the molecule. (We call this "approximate occurrence.") The proposed method combines the geometric hashing technique and block detection algorithms for undirected graphs. To demonstrate the utility of our algorithms, we discuss their applications to classifying three families of molecules pertaining to antibacterial sulfa drugs, anti-anxiety agents (benzodiazepines) and antiadrenergic agents (β receptors). Experimental results indicate the good performance of our algorithms and the high quality of the discovered motife.