12513185
Classifying Objectionable Websites Based on Image Content
This paper describes IBCOW (Image-based Classification of Objectionable Websites), a system capable of classifying a website as objectionable or benign based on image content. The system uses WIPEℳ (Wavelet Image Pornography Elimination) and statistics to provide robust classification of on-line objectionable World Wide Web sites. Semantically-meaningful feature vector matching is carried out so that comparisons between a given on-line image and images marked as ”objectionable” and ”benign” in a training set can be performed efficiently and effectively in the WIPE module. If more than a certain number of images sampled from a site is found to be objectionable, then the site is considered to be objectionable. The statistical analysis for determining the size of the image sample and the threshold number of objectionable images is given in this paper. The system is practical for real-world applications, classifying a Web site at a speed of less than 2 minutes each, including the time to compute the feature vector for the images downloaded from the site, on a Pentium Pro PC. Besides its exceptional speed, it has demonstrated 97% sensitivity and 97% specificity in classifying a Web site based solely on images. Both the sensitivity and the specificity in real-world applications is expected to be higher because our performance evaluation is relatively conservative and surrounding text can be used to assist the classification process.
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.
1791876
A Region-Based Fuzzy Feature Matching Approach to Content-Based Image Retrieval
This paper proposes a fuzzy logic approach, UFM (unified feature matching), for region-based image retrieval. In our retrieval system, an image is represented by a set of segmented regions, each of which is characterized by a fuzzy feature (fuzzy set) reflecting color, texture, and shape properties. As a result, an image is associated with a family of fuzzy features corresponding to regions. Fuzzy features naturally characterize the gradual transition between regions (blurry boundaries) within an image and incorporate the segmentation-related uncertainties into the retrieval algorithm. The resemblance of two images is then defined as the overall similarity between two families of fuzzy features and quantified by a similarity measure, UFM measure, which integrates properties of all the regions in the images. Compared with similarity measures based on individual regions and on all regions with crisp-valued feature representations, the UFM measure greatly reduces the influence of inaccurate segmentation and provides a very intuitive quantification. The UFM has been implemented as a part of our experimental SIMPLIcity image retrieval system. The performance of the system is illustrated using examples from an image database of about 60,000 general-purpose images.
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.
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.
42707225
A Web-based Course in English as a Second Language: A Case Study
46167609
On solving the face recognition problem with one training sample per subject
The lack of adequate training samples and the considerable variations observed in the available image collections due to aging, illumination and pose variations are the two key technical barriers that appearance-based face recognition solutions have to overcome. It is a well-documented fact that their performance deteriorates rapidly when the number of training samples is smaller than the dimensionality of the image space. This is especially true for face recognition applications where only one training sample per subject is available. In this paper, a recognition framework based on the concept of the so-called generic learning is introduced as an attempt to boost the performance of traditional appearance-based recognition solutions in the one training sample application scenario. Different from contemporary approaches, the proposed solution learns the intrinsic properties of the subjects to be recognized using a generic training database which consists of images from subjects other than those under consideration. Many state-of-the-art face recognition solutions can be readily integrated in the proposed framework. A novel multi-learner framework is also proposed to further boost recognition performance. Extensive experimentation reported in the paper suggests that the proposed framework provides a comprehensive solution and achieves lower error recognition rate when considered in the context of one training sample face recognition problem.
11945508
Content-Based Audio Classification Using Support Vector Machines and Independent Component Analysis
In this paper, we present a new audio classification system. First, a frame-based multiclass support vector machine (SVM) for audio classification is proposed. The accuracy rate has significant improvements over conventional file-based SVM audio classifier. In feature selection, this study transforms the log powers of the critical-band filters based on independent component analysis (ICA). This new audio feature is combined with mel-frequency cepstral coefficients (MFCCs) and five perceptual features to form an audio feature set. The superiority of the proposed system has been demonstrated via a 15-class sound database with a 91.7% accuracy rate
5763734
Finding approximate patterns in undirected acyclic graphs
Abstract We consider an approximate pattern matching problem for undirected acyclic graphs. Specifically, let P be a pattern graph, D a data graph and t an integer. We present an algorithm to locate a subgraph in D whose distance from P is at most t . The distance measure used here is the degree-2 metric published previously. The time complexity of our algorithm is O(N P N D d d log d) where N P and N D are the number of nodes in P and D , respectively; d =min{ d P , d D }; d P and d D are the maximum degree of P and D , respectively. Central to our algorithm is a procedure for finding approximate patterns in rooted unordered trees and freely allowing cuts. We discuss two applications of the algorithms in chemical information search and website management on the Internet.
37176365
A content-based scheme for CT lung image retrieval
A content based scheme to retrieve computed tomographic images (CT) of the lung is presented. It consists of a visual based user interface to allow the query be made by line drawing the interested (abnormal) regions; and a training scheme to classify the relationship between the images stored in database. The system will output a set of candidate images that are texturally similar to the query image. We marked the abnormal portions of each training image by a polygonal or rectangular shape manually because it requires expert knowledge. Then, the texture features of each marked region are extracted on the DCT or SADCT transform domain. In the training stage, the extracted DCT/SADCT coefficients are fed into a Kohonen self organizing network to find the relationship for classification. In the query stage, the system first checks which texture category the query image is in, then uses some geometrical characteristics to identify the most likely candidate image. Experimental results show that 96% of the queries can be correctly retrieved where the original image is in the candidate set.