14383826
Wavelet-based image indexing techniques with partial sketch retrieval capability
This paper describes WBIIS (Wavelet-Based Image Indexing and Searching), a new image indexing and retrieval algorithm with partial sketch image searching capability for large image databases. The algorithm characterizes the color variations over the spatial extent of the image in a manner that provides semantically-meaningful image comparisons. The indexing algorithm applies a Daubechies' wavelet transform for each of the three opponent color components. The wavelet coefficients in the lowest few frequency bands, and their variances, are stored as feature vectors. To speed up retrieval: a two-step procedure is used that first does a crude selection based on the variances, and then refines the search by performing a feature vector match between the selected images and the query. For better accuracy in searching, two level multiresolution matching may also be used. Masks are used for partial-sketch queries. This technique performs much better in capturing coherence of image, object granularity, local color/texture, and bias avoidance than traditional color layout algorithms. When tested on a database of more than 10,000 general-purpose images, WBIIS is much faster and more accurate than traditional algorithms.
14843520
Deriving knowledge from figures for digital libraries
Figures in digital documents contain important information. Current digital libraries do not summarize and index information available within figures for document retrieval. We present our system on automatic categorization of figures and extraction of data from 2-D plots. A machine-learning based method is used to categorize figures into a set of predefined types based on image features. An automated algorithm is designed to extract data values from solid line curves in 2-D plots. The semantic type of figures and extracted data values from 2-D plots can be integrated with textual information within documents to provide more effective document retrieval services for digital library users. Experimental evaluation has demonstrated that our system can produce results suitable for real-world use.
2511396
An architecture for creating collaborative semantically capable scientific data sharing infrastructures
Increasingly, scientists are seeking to collaborate and share data among themselves. Such sharing is can be readily done by publishing data on the World-Wide Web. Meaningful querying and searching on such data depends upon the availability of accurate and adequate metadata that describes the data and the sources of the data. In this paper, we outline the architecture of an implemented cyber-infrastructure for chemistry that provides tools for users to upload datasets and their metadata to a database. Our proposal combines a two level metadata system with a centralized database repository and analysis tools to create an effective and capable data sharing infrastructure. Our infrastructure is extensible in that it can handle data in different formats and allows different analytic tools to be plugged in.
2269151
Content-based image indexing and searching using Daubechies' wavelets
Abstract. This paper describes WBIIS (Wavelet-Based Image Indexing and Searching), a new image indexing and retrieval algorithm with partial sketch image searching capability for large image databases. The algorithm characterizes the color variations over the spatial extent of the image in a manner that provides semantically meaningful image comparisons. The indexing algorithm applies a Daubechies' wavelet transform for each of the three opponent color components. The wavelet coefficients in the lowest few frequency bands, and their variances, are stored as feature vectors. To speed up retrieval, a two-step procedure is used that first does a crude selection based on the variances, and then refines the search by performing a feature vector match between the selected images and the query. For better accuracy in searching, two-level multiresolution matching may also be used. Masks are used for partial-sketch queries. This technique performs much better in capturing coherence of image, object granularity, local color/texture, and bias avoidance than traditional color layout algorithms. WBIIS is much faster and more accurate than traditional algorithms. When tested on a database of more than 10 000 general-purpose images, the best 100 matches were found in 3.3 seconds.
10201400
RF/sup */IPF: a weighting scheme for multimedia information retrieval
The region-based approach has become a popular research trend in the field of multimedia database retrieval. We present the Region Frequency and Inverse Picture Frequency (RF/sup */IPF) weighting, a measure developed to unify region-based multimedia retrieval systems with text-based information retrieval systems. The weighting measure gives the highest weight to regions that occur often in a small number of images in the database. These regions are considered discriminators. With this weighting measure, we can blend image retrieval techniques with TF/sup */IDF-based text retrieval techniques for large-scale Web applications. The RF/sup */IPF weighting has been implemented as a part of our experimental SIMPLIcity image retrieval system and tested on a database of about 200000 general-purpose images. Experiments have shown that this technique is effective in discriminating images of different semantics. Additionally, the overall similarity approach enables a simple querying interface for multimedia information retrieval systems.
4227250
Content-based image retrieval by clustering
In a typical content-based image retrieval (CBIR) system, query results are a set of images sorted by feature similarities with respect to the query. However, images with high feature similarities to the query may be very different from the query in terms of semantics. This is known as the semantic gap. We introduce a novel image retrieval scheme, CLUster-based rEtrieval of images by unsupervised learning (CLUE), which tackles the semantic gap problem based on a hypothesis: semantically similar images tend to be clustered in some feature space. CLUE attempts to capture semantic concepts by learning the way that images of the same semantics are similar and retrieving image clusters instead of a set of ordered images. Clustering in CLUE is dynamic. In particular, clusters formed depend on which images are retrieved in response to the query. Therefore, the clusters give the algorithm as well as the users semantic relevant clues as to where to navigate. CLUE is a general approach that can be combined with any real-valued symmetric similarity measure (metric or nonmetric). Thus it may be embedded in many current CBIR systems. Experimental results based on a database of about 60, 000 images from COREL demonstrate improved performance.
1269749
Complexity-adaptive search algorithm for block motion estimation
We propose a complexity-adaptive fast block matching algorithm design strategy that allows the user to terminate the algorithm at any target computational complexity. Two complexity-adaptive techniques, frame level complexity allocation and block level complexity allocation, are developed to approach the global complexity-distortion optimization A buffer control strategy is proposed to dynamically adjust the target complexity of one frame. The predictive complexity-distortion benefit list (PCDB list) technique is employed to allocate the target complexity into each block. By these techniques, we modify the full-search block matching, three-step search, new three-step search, and four-step search to the complexity-adaptive algorithms. Experimental results show that the complexity-adaptive algorithms could achieve better performance than traditional fixed algorithms from the viewpoint of complexity-distoftion optimization.
207558887
Self-adaptive neuro-fuzzy systems for autonomous underwater vehicle control
This paper presents a systematic approach for developing a concise self-adaptive neurofuzzy inference system (SANFIS) with a fast hybrid parameter learning algorithm for on-line learning of the control knowledge for autonomous underwater vehicle (AUV) control. The multi-layered network structure of SANFIS incorporates fuzzy basis functions for better function approximations. We investigate three SANFIS structures with three different types of fuzzy IF-THEN rule-based models and cast the rule formation problem as a clustering problem. A recursive least-squares algorithm and a modified Levenberg-Marquardt algorithm with limited memory are exploited to accelerate the parameter learning process. This hybrid parameter learning algorithm together with an on-line clustering technique and rule examination provide SANFIS with the capability of selforganizing and self-adapting its internal structure (i.e. the fuzzy rules and term sets) for learning the required control knowledge for an AUV to follow desired trajectories. Computer simulations for modeling a control system for an AUV have been conducted to validate the effectiveness of the proposed SANFIS.
34826531
User-assisted query translation for interactive CLIR
We view interactive Cross-Language Information Retrieval (CLIR) as an iterative process in which the searcher and the retrieval system collaborate to find documents that satisfy the searcher’s needs, regardless of the language in which those documents are written. Our motivation is that humans and machines can bring complementary strengths to this process. Machines are excellent at repetitive tasks that are well specified; humans bring creativity and exceptional pattern recognition capabilities. Properly coupling these capabilities can result in a synergy that greatly exceeds the ability of either human or machine alone. Designers of CLIR systems can select from a variety of fully automatic techniques to overcome problems with unknown terms and translation ambiguity, but automatic processing of this sort risks reducing the searcher’s understanding of system operation. This, in turn, tends to work against the synergy that we seek to accomplish. We are therefore exploring more transparent approaches to support interactive CLIR.
14858727
BIO-AJAX: an extensible framework for biological data cleaning
As databases become more pervasive through the biological sciences, various data quality issues regarding data legacy, data uniformity and data duplication arise. Due to the nature of this data, each of these problems is non-trivial. For biological data to be corrected and standardized, new methods and frameworks must be developed. This paper proposes one such framework, called BIO-AJAX, which uses principles from data cleaning to improve data quality in biological information systems, specifically in TreeBASE.
2366018
Constrained texture synthesis by scalable sub-patch algorithm
In this paper, we propose a novel scalable sub-patch algorithm for texture synthesis. Texture synthesis is an important research topic in computer graphics and image processing. We present an efficient algorithm which can paste a patch each time without boundary processing technique. In particular, the proposed sub-patch algorithm can be used in constrained texture synthesis to fill the large lost region. On the other hand, the searching neighborhood pixels can be changed due to different patch size in image reconstruction. The comparison of previous algorithms is also provided in this paper. We present a first algorithm used in constrained texture synthesis by regular synthesizing order