2592435
Automatic image captioning
We examine the problem of automatic image captioning. Given a training set of captioned images, we want to discover correlations between image features and keywords, so that we can automatically find good keywords for a new image. We experiment thoroughly with multiple design alternatives on large datasets of various content styles, and our proposed methods achieve up to a 45% relative improvement on captioning accuracy over the state of the art.
10596767
Automatic mining of fruit fly embryo images
We present FEMine, an automatic system for image-based gene expression analysis. We perform experiments on the largest publicly available collection of Drosophila ISH (in situ hybridization) images, showing that our FEMine system achieves excellent performance in classification, clustering, and content-based image retrieval. The major innovation of FEMine is the use of automatically discovered latent spatial "themes" of gene expressions, LGEs, in the whole-embryo context, as opposed to patterns in nearly disjoint portions of an embryo proposed in previous methods.
7681917
ViVo: visual vocabulary construction for mining biomedical images
Given a large collection of medical images of several conditions and treatments, how can we succinctly describe the characteristics of each setting? For example, given a large collection of retinal images from several different experimental conditions (normal, detached, reattached, etc.), how can data mining help biologists focus on important regions in the images or on the differences between different experimental conditions? If the images were text documents, we could find the main terms and concepts for each condition by existing IR methods (e.g., tf/idf and LSI). We propose something analogous, but for the much more challenging case of an image collection: We propose to automatically develop a visual vocabulary by breaking images into n /spl times/ n tiles and deriving key tiles ("ViVos") for each image and condition. We experiment with numerous domain-independent ways of extracting features from tiles (color histograms, textures, etc.), and several ways of choosing characteristic tiles (PCA, ICA). We perform experiments on two disparate biomedical datasets. The quantitative measure of success is classification accuracy: Our "ViVos" achieve high classification accuracy (up to 83 %for a nine-class problem on feline retinal images). More importantly, qualitatively, our "ViVos" do an excellent job as "visual vocabulary terms": they have biological meaning, as corroborated by domain experts; they help spot characteristic regions of images, exactly like text vocabulary terms do for documents; and they highlight the differences between pairs of images.
34366886
AutoSplit: Fast and Scalable Discovery of Hidden Variables in Stream and Multimedia Databases
For discovering hidden (latent) variables in real-world, non-gaussian data streams or an n-dimensional cloud of data points, SVD suffers from its orthogonality constraint. Our proposed method, “AutoSplit”, finds features which are mutually independent and is able to discover non-orthogonal features. Thus, (a) finds more meaningful hidden variables and features, (b) it can easily lead to clustering and segmentation, (c) it surprisingly scales linearly with the database size and (d) it can also operate in on-line, single-pass mode. We also propose “Clustering-AutoSplit”, which extends the feature discovery to multiple feature/bases sets, and leads to clean clustering. Experiments on multiple, real-world data sets show that our method meets all the properties above, outperforming the state-of-the-art SVD.
1997586
Automatic multimedia cross-modal correlation discovery
Given an image (or video clip, or audio song), how do we automatically assign keywords to it? The general problem is to find correlations across the media in a collection of multimedia objects like video clips, with colors, and/or motion, and/or audio, and/or text scripts. We propose a novel, graph-based approach, "MMG", to discover such cross-modal correlations.Our "MMG" method requires no tuning, no clustering, no user-determined constants; it can be applied to any multimedia collection, as long as we have a similarity function for each medium; and it scales linearly with the database size. We report auto-captioning experiments on the "standard" Corel image database of 680 MB, where it outperforms domain specific, fine-tuned methods by up to 10 percentage points in captioning accuracy (50% relative improvement).
206616426
VideoCube: A Novel Tool for Video Mining and Classification
We propose a new tool to classify a video clip into one of n given classes (e.g., "news", "commercials", etc). The first novelty of our approach is a method to automatically derive a "vocabulary" from each class of video clips, using the powerful method of "Independent Component Analysis" (ICA). Second, the method is unified in that it works with both video and audio information, and gives vocabulary describing not only the still images, but also motion and the audio part. Further-more, this vocabulary is natural in that it is closely related to human perceptual processing. More specifically, every class of video clips gives a list of "basis functions", which can compress its members very well. Once we represent video clips in "vocabularies", we can do classification and pattern discovery. For the classification of a video clip, we propose using compression: we test which of the "vocabularies" can compress the video clip best, and we assign it to the corresponding class.For data mining, we inspect the basis functions of each video genre class and identify genre characteristics such as fast motions/transitions, more harmonic audio, etc. In experiments on real data of 62 news and 43 commercial clips, our method achieved overall accuracy of ? 81%.
13419311
Metamodeling Architecture of Web Ontology Languages
Recent research has shown that RDF Schema, as a schema layer Semantic Web language, has a non-standard metamodeling architecture. As a result, it is difficult to understand and lacks clear semantics. This paper proposes a fixed layer metamodeling architecture for RDF Schema (RDFS(FA)) and demonstrates how the problems of RDF Schema can be solved under RDFS(FA). Based on this metamodeling architecture, a clear model-theoretic semantics of RDFS(FA) is given. Interestingly, RDFS(FA) also benefits DAML+OIL by offering a firm semantic basis and by solving the "layer mistake" problem.
15318471
Ant colony system with communication strategies
In this paper an ant colony system (ACS) with communication strategies is developed. The artificial ants are partitioned into several groups. Seven communication methods for updating the pheromone level between groups in ACS are proposed and work on the traveling salesman problem using our system is presented. Experimental results based on three well-known traveling salesman data sets demonstrate the proposed ACS with communication strategies are superior to the existing ant colony system (ACS) and ant system (AS) with similar or better running times.
6653256
Domain-constrained semi-supervised mining of tracking models in sensor networks
Accurate localization of mobile objects is a major research problem in sensor networks and an important data mining application. Specifically, the localization problem is to determine the location of a client device accurately given the radio signal strength values received at the client device from multiple beacon sensors or access points. Conventional data mining and machine learning methods can be applied to solve this problem. However, all of them require large amounts of labeled training data, which can be quite expensive. In this paper, we propose a probabilistic semi supervised learning approach to reduce the calibration effort and increase the tracking accuracy. Our method is based on semi-supervised conditional random fields which can enhance the learned model from a small set of training data with abundant unlabeled data effectively. To make our method more efficient, we exploit a Generalized EM algorithm coupled with domain constraints. We validate our method through extensive experiments in a real sensor network using Crossbow MICA2 sensors. The results demonstrate the advantages of methods compared to other state-of-the-art object-tracking algorithms.
6604472
A Fine-Grained Approach to Resolving Unsatisfiable Ontologies
In the semantic Web, inconsistencies in OWL ontologies may easily occur. Existing approaches either identify the minimally unsatisfiable sub-ontologies or calculate the maximally satisfiable sub-ontologies. However practical problems remain; it is not clear which axioms or which parts of axioms should be selected for repair, and how to repair those axioms. In this paper, we address this limitation by proposing a fine-grained approach to resolving unsatisfiable ontologies. We revise the axiom tracing technique first proposed by Baader and Hollunder, so as to track which parts of the problematic axioms cause the unsatisfiability. Moreover, we support ontology users in rewriting problematic axioms. In order to minimise the impact of changes and prevent unintended entailment loss, harmful and helpful changes are identified and provided as guidelines. Based on the methods described we present a preliminary version of an interactive debugging tool and demonstrate its applicability in practice
16432489
Genetic watermarking based on transform-domain techniques
Abstract An innovative watermarking scheme based on genetic algorithms (GA) in the transform domain is proposed. It is robust against watermarking attacks, which are commonly employed in the literature. In addition, the watermarked image quality is also considered. In this paper, we employ GA for optimizing both the fundamentally conflicting requirements. Watermarking with GA is easy for implementation. We also examine the effectiveness of our scheme by checking the fitness function in GA, which includes both factors related to robustness and invisibility. Simulation results also show both the robustness under attacks, and the improvement in watermarked image quality with GA.