MOTIVATIONS: Technological advances in biomedical research are generating a plethora of heterogeneous data at a high rate. There is a critical need for extraction, integration and management tools for information discovery and synthesis from these heterogeneous data. RESULTS: In this paper, we present a general architecture, called ALFA, for information extraction and representation from diverse biological data. The ALFA architecture consists of: (i) a networked, hierarchical, hyper-graph object model for representing information from heterogeneous data sources in a standardized, structured format; and (ii) a suite of integrated, interactive software tools for information extraction and representation from diverse biological data sources. As part of our research efforts to explore this space, we have currently prototyped the ALFA object model and a set of interactive software tools for searching, filtering, and extracting information from scientific text. In particular, we describe BioFerret, a meta-search tool for searching and filtering relevant information from the web, and ALFA Text Viewer, an interactive tool for user-guided extraction, disambiguation, and representation of information from scientific text. We further demonstrate the potential of our tools in integrating the extracted information with experimental data and diagrammatic biological models via the common underlying ALFA representation. CONTACT: [email protected].

We present an algorithm for automatic image orientation estimation using a Bayesian learning framework. We demonstrate that a small codebook (the optimal size of codebook is selected using a modified MDL criterion) extracted from a learning vector quantizer (LVQ) can be used to estimate the class-conditional densities of the observed features needed for the Bayesian methodology. We further show how principal component analysis (PCA) and linear discriminant analysis (LDA) can be used as a feature extraction mechanism to remove redundancies in the high-dimensional feature vectors used for classification. The proposed method is compared with four different commonly used classifiers, namely k-nearest neighbor, support vector machine (SVM), a mixture of Gaussians, and hierarchical discriminating regression (HDR) tree. Experiments on a database of 16 344 images have shown that our proposed algorithm achieves an accuracy of approximately 98% on the training set and over 97% on an independent test set. A slight improvement in classification accuracy is achieved by employing classifier combination techniques.

Grouping images into (semantically) meaningful categories using low-level visual features is a challenging and important problem in content-based image retrieval. Using binary Bayesian classifiers, we attempt to capture high-level concepts from low-level image features under the constraint that the test image does belong to one of the classes. Specifically, we consider the hierarchical classification of vacation images; at the highest level, images are classified as indoor or outdoor; outdoor images are further classified as city or landscape; finally, a subset of landscape images is classified into sunset, forest, and mountain classes. We demonstrate that a small vector quantizer (whose optimal size is selected using a modified MDL criterion) can be used to model the class-conditional densities of the features, required by the Bayesian methodology. The classifiers have been designed and evaluated on a database of 6931 vacation photographs. Our system achieved a classification accuracy of 90.5% for indoor/outdoor, 95.3% for city/landscape, 96.6% for sunset/forest and mountain, and 96% for forest/mountain classification problems. We further develop a learning method to incrementally train the classifiers as additional data become available. We also show preliminary results for feature reduction using clustering techniques. Our goal is to combine multiple two-class classifiers into a single hierarchical classifier.

We have developed a reject option for VQ-based supervised Bayesian classification to improve classification accuracy by sieving out patterns that are classified with a low confidence value. A small codebook extracted from a learning vector quantizer (LVQ) is used to estimate the class-conditional densities of the feature vector. We adapt the two commonly used rejection criteria, outlier rejection and ambiguity rejection, for the VQ-based Bayesian classifiers. Using three high-level image classification problems, we demonstrate how local rejection criteria can improve the error vs. reject characteristics of our classifier over a global rejection method.

Grouping images into (semantically) meaningful categories using low-level visual features is a challenging and important problem in content-based image retrieval. In this paper, we develop an incremental learning paradigm for Bayesian classification of images. Under the Bayesian paradigm, the class-conditional densities are represented in terms of codebook vectors. Learning is thus incrementally updating these codebook vectors as new training data become available. The proposed learning scheme estimates the already learnt training samples from the existing codebook vectors and augments these to the new training set for re-training the classifier. The above paradigm is shown to yield good results on three complex image classification problems. A classifier trained incrementally has comparable accuracies to the one which is trained using the true training samples.

We present an algorithm for automatic image orientation estimation using a Bayesian learning framework. We demonstrate that a small codebook (the optimal size of codebook is selected using a modified MDL criterion) extracted from a vector quantizer can be used to estimate the class-conditional densities of the observed features needed for the Bayesian methodology. We further show how feature clustering can be used as a feature selection mechanism to remove redundancies in the high-dimensional feature vectors used for classification. Experiments on a database of 17,901 images have shown that our proposed algorithm achieves an accuracy of approximately 97% on the training set and over 89% on an independent test set.

Typical video search is based on queries involving a single shot. We generalize this problem by allowing queries that involve a video clip. We propose two schemes for query by video clip. In the first scheme, retrieval based on key frames where the database and query video are segmented into shots which are represented via key frames. For every query key frame, a similarity value (using color, texture, and motion) is associated with the key frames in the database video clip. Boundaries marking highly similar consecutive shots are then used to generate the set of retrieved video sub-clips. In the second scheme, in retrieval using sub-sampled frames, we uniformly sub-sample the query clip as well as the database video. The retrieval is based on matching color and texture features of the sub-sampled frames. Initial experiments on two video databases show promising results.

Establishing the identity of the craftsmen of old Norwegian silver objects can be accomplished through the recognition of master-marks. Manually searching through the 6000 master-marks and 250 city-marks is tedious and unreliable. Our-goal is to develop an efficient retrieval of images of master- and city-marks from large databases based on the shape content of the image. We are building an automatic retrieval system to browse through the entire database. The first step is to extract primitive visual features from the images and to retrieve images on the basis of these features. We have developed a prototype image database of master-marks from a catalog by scanning 171 of these marks which appear on silver tankards. We have successfully extracted shape features from these marks and matched master-marks in the database against the unknown query images. Initial experiments on master-marks are encouraging. The same shape-based matching techniques are used to match city-marks. Our future work will involve building an expert system for a stylistic analysis of the silver tankards.

Retrieval efficiency and accuracy are two important issues in designing a content-based database retrieval system. We propose a new image database retrieval method based on shape information. This system achieves both the desired efficiency and accuracy using a two-stage hierarchy: in the first stage, simple and easily computable statistical shape features are used to quickly browse through the database to generate a moderate number of plausible retrievals; in the second stage, the outputs from the first stage are screened using a deformable template matching process to discard spurious matches. We have tested the algorithm using hand drawn queries on a trademark database containing 1,100 images. Each retrieval takes a reasonable amount of computation time. The top most retrieved image from the system agrees with that obtained by human subjects, but there are significant differences between the top 10 retrieved images by our system and that provided by human subjects. This demonstrates the need for developing shape features that are better able to capture human perceptual similarity of shapes.