Recent advances in computing, communications and storage technology have made multimedia data become prevalent. Multimedia has gained enormous potential in improving the processes in a wide range of fields, such as advertising and marketing, education and training, entertainment, medicine, surveillance, wearable computing, biometrics, and remote sensing. Rich content of multimedia data, built through the synergies of the information contained in different modalities, calls for new and innovative methods for modeling, processing, mining, organizing, and indexing of this data for effective and efficient searching, retrieval, delivery, management and sharing of multimedia content, as required by the applications in the abovementioned fields. The objective of this paper is to present our views on the trends that should be followed when developing such methods, to elaborate on the related research challenges, and to introduce the new conference, Multimedia Content Analysis, Management and Retrieval, as a premium venue for presenting and discussing these methods with the scientific community. Starting from 2006, the conference will be held annually as a part of the IS&T/SPIE Electronic Imaging event.
VIMA has experienced an increasing demand for Content-based Image Retrieval (CBIR) systems since late 2004. In this paper, we report the search, filtering, and annotation systems that we have developed and deployed, and the user models of these systems. The objective of this paper is to provide to the researchers and developers in the area of image retrieval, guidelines for measuring the performance of their algorithms/systems, in a way that is consonant with the requirements of the users. We also enumerate technical challenges of building CBIR systems, and outline our solutions to tackle these challenges.
This paper presents an event sensing paradigm for intelligent event-analysis in a wireless, ad hoc, multi-camera, video surveillance system. In particilar, we present statistical methods that we have developed to support three aspects of event sensing: 1) energy-efficient, resource-conserving, and robust sensor data fusion and analysis, 2) intelligent event modeling and recognition, and 3) rapid deployment, dynamic configuration, and continuous operation of the camera networks. We outline our preliminary results, and discuss future directions that research might take.
By extracting representative image features and employing our recently developed perceptual distance function (dynamic partial function), image copy detection can be performed effectively. Our empirical study shows that our scheme can detect various forms of near-replicas with high accuracy. Thus, our system has application for protection of copyrighted images and trademarks.
Using video analysis for detecting hazardous events such as fire/smoke activity, impending threats, or suspicious behaviors has spurred new research for security concerns. To make such detection reliable, researchers must overcome difficulties such as classification by the importance of consequences, imbalances of positive and negative data, environmental factors, and variation in camera capabilities. This paper puts forward a general framework for hazardous event detection which includes spatial-temporal feature extraction, statistical-based classification for biased data and calibration for environmental change. At the current stage of development, the framework can work effectively for detecting hazardous events like fire/smoke from video sequences.
Traditional content-based image retrieval (CBIR) systems suffer from at least two shortcomings. First, most systems require users to provide good images to initiate queries. We argue that finding good seeds is the job of the search engine itself, and this circular requirement leaves the core problem---understanding users' query concepts---unsolved. The second shortcoming is that most systems fail to adequately model users' subjectivity. This paper proposes two test queries to Benchathlon for steering the CBIR community to seriously address the above issues. We also present a search scenario to show how such queries may be supported and their performance be measured.
Internet piracy has been one of the major concerns for Web publishing. In this study we present a system, RIME, that we have prototyped for detecting unauthorized image copying on the WWW. To speed up the copy detection, RIME uses a new clustering/hashing approach that first clusters similar images on adjacent disk cylinders and then builds indexes to access the clusters made in this way. Searching for the replicas of an image often takes just one IO to loop up the location of the cluster containing similar objects and one sequential file IO to read in this cluster. Our experimental results show that RIME can detect images copies both more efficiently and effectively than the traditional content- based image retrieval systems that use tree-like structures to index images. In addition, RIME copes well with image format conversion, resampling, requantization and geometric transformation.
This paper describes RIME (Replicated IMage dEtector), an alternative approach to watermarking for detecting unauthorized image copying on the Internet. RIME profiles internet images and stores the feature vectors of the images and their URLs in its repository. When a copy detection request is received, RIME matches the requested image's feature vector with the vectors stored in the repository and returns a list of suspect URLs. RIME characterizes each image using Daubechies' wavelets. The wavelet coefficients are stored as the feature vector. RIME uses a multidimensional extensible hashing scheme to index these high-dimensional feature vectors. Our preliminary result shows that it can detect image copies effectively. It can find the top suspects and copes well with image format conversion, resampling, and requantization.