Retrieval efficiency and accuracy are two important issues in designing a content-based database retrieval system. We propose a method for trademark image database retrieval based on object shape information that would supplement traditional text-based retrieval systems. This system achieves both the desired efficiency and accuracy using a two-stage hierarchy: in the first stage, simple and easily computable shape features are used to quickly browse through the database to generate a moderate number of plausible retrievals when a query is presented; in the second stage, the candidates 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 (¸ 4-5 seconds on a Sun Sparc 20 workstation). The top most image retrieved by the system agrees with that obtained by human subjects, ...

The recognition of objects from imagery in a manner that is independent of scale, posi-tion, and orientation may be achieved by characterizing an object with a set of extracted invariant features. Several different recognition techniques have been demonstrated that utilize moments to generate such invariant features. These techniques are derived from general moment theory that is widely used throughout statistics and mechanics. In this paper, basic Cartesian moment theory is reviewed and its application to object recognition and image analysis is presented. The geometric properties of low-order moments are discussed along with the definition of several moment-space linear geometric transforms. Finally, significant research in moment-based object recognition is reviewed. 1.

A large number of 3D models are created and available on the Web, since more and more 3D modelling and digitizing tools are developed for ever increasing applications. The techniques for content-based 3D model retrieval then become necessary. In this paper, a visual similarity-based 3D model retrieval system is proposed.

A great deal of work has been done on the evaluation of information retrieval systems for alphanumeric data. The same thing can not be said about the newly emerging multimedia and image database systems. One of the central concerns in these systems is the automatic characterization of image content and retrieval of images based on similarity of image content. In this paper, we discuss effectiveness of several shape measures for content based similarity retrieval of images. The different shape measures we have implemented include outline based features (chain code based string features, Fourier descriptors, UNL Fourier features), region based features (invariant moments, Zemike moments, pseudo-Zemike moments), and combined features (invariant moments & Fourier descriptors, invariant moments & UNL Fourier features). Given an image, all these shape feature measures (vectors) are computed automatically, and the feature vector can either be used for the retrieval purpose or can be stored in the database for future queries. We have tested all of the above shape features for image retrieval on a database of 500 trademark images. The average retrieval efficiency values computed over a set of fifteen representative queries for all the methods is presented. The output of a sample shape similarity query using all the features is also shown.

most challenging research issues in video data management. To go beyond low-level similarity and access video data content by semantics, we need to bridge the gap between the low-level representation and high-level semantics. This is a difficult multimedia understanding problem. We formulate this problem as a probabilistic pattern-recognition problem for modeling semantics in terms of concepts and context. To map low-level features to high-level semantics, we propose probabilistic multimedia objects (multijects). Examples of multijects in movies include explosion, mountain, beach, outdoor, music, etc. Semantic concepts in videos interact and appear in context. To model this interaction explicitly, we propose a network of multijects (multinet). To model the multinet computationally, we propose a factor graph framework which can enforce spatio-temporal constraints. Using probabilistic models for multijects, rocks, sky, snow, water-body, and forestry/greenery, and using a factor graph as the multinet, we demonstrate the application of this framework to semantic video indexing. We demonstrate how detection performance can be significantly improved using the multinet to take inter-conceptual relationships into account. Our experiments using a large video database consisting of clips from several movies and based on a set of five semantic concepts reveal a significant improvement in detection performance by over 22%. We also show how the multinet is extended to take temporal correlation into account. By constructing a dynamic multinet, we show that the detection performance is further enhanced by as much as 12%. With this framework, we show how keyword-based query and semantic filtering is possible for a predetermined set of concepts. Index Terms—Factor graphs, hidden Markov models, likelihood ratio test, multimedia understanding, probabilistic graphical networks, probability propagation, query by example, query by keywords, ROC curves, semantic video indexing, sum-product algorithm. I.

We describe a method for shape-based image database search that uses deformable prototypes to represent categories. Rather than directly comparing a candidate shape with all shape entries in the database, shapes are compared in terms of the types of nonrigid deformations (differences) that relate them to a small subset of representative prototypes. To solve the shape correspondence and alignment problem, we employ the technique of modal matching, an information-preserving shape decomposition for matching, describing, and comparing shapes despite sensor variations and nonrigid deformations. In modal matching, shape is decomposed into an ordered basis of orthogonal principal components. We demonstrate the utility of this approach for shape comparison in 2-D image databases.

A central problem in object recognition is to determine the transformation that relates the model to the image, given some partial correspondence between the two. This is useful in determining whether an object is present in an image, and if so, determining where the object is. We present a novel method of solving this problem that uses region information. In our approach the model is divided into volumes, and the image is divided into regions. Given a match between subsets of volumes and regions (without any explicit correspondence between different pieces of the regions) the alignment transformation is computed. The method applies to planar objects under similarity, affine, and projective transformations and to projections of 3-D objects undergoing affine and projective transformations. 1 Introduction A fundamental problem in recognition is pose estimation. Given a correspondence between some portions of an object model and some portions of an image, determine the transformation th...

In this paper we present a learning-based approach to semantic indexing of multimedia content using cues derived from audio, visual and text features. We approach the problem by developing a set of statistical models for a predefined lexicon. Novel concepts are then mapped in terms of concepts in the lexicon. To achieve robust detection of concepts, we exploit features from multiple modalities, namely audio, visual and text. Concept representations are modeled using Gaussian Mixtures (GMM), Hidden Markov Models (HMM), and Support Vector Machines (SVM). Models such as Bayesian Networks and SVMs are used in a late fusion approach to model concepts that are not explicitly modeled in terms of features. Our experiments indicate promise in the proposed classification and fusion methodologies: Our proposed fusion scheme achieves more than 10 % relative improvement over the best uni-modal concept detector. 1

Introduction The ever expanding gamut of medical imaging techniques provides the clinician an increasingly multifaceted view of brain function and anatomy. The information provided by the various imaging modalities is often complementary (i.e. provides separate but useful information) and synergistic (i.e. the combination of information provides useful extra information). For example, X-ray computed tomography (CT) and magnetic resonance (MR) imaging exquisitely demonstrate brain anatomy but provide little functional information. Positron emission tomography (PET) and single photon emission computed tomography (SPECT) scans display aspects of brain function and allow metabolic measurements but poorly delineate anatomy. Furthermore, CT and MR images describe complementary morphologic features. For example, bone and calcifications are best seen on CT images, while soft-tissue structures are better differentiated by MR imaging. Clinical diagnosis and therapy planning and evaluatio

The problem of selecting the best set of features for target recognition using a multilayer perceptron is addressed in this paper. A technique has been developed which analyzes the weights in a multilayer perceptron to determine which features the network finds important and which are unimportant. A brief introduction to the use of multilayer perceptrons for classification and the training rules available is followed by the mathematical development of the saliency measure for multilayer perceptrons. The technique is applied to two different image databases and is found to be consistent with statistical techniques and independent of the network initial conditions. The saliency measure is then used to compare the results of two different training rules on a target recognition problem. 1 Introduction Recently there has been a great deal of interest in the use of multilayer perceptrons as classifiers in pattern recognition problems (see, for example, [11]). Unfortunately, little ...