Dennis Koelma

The Semantic Pathfinder: Using an Authoring Metaphor for Generic Multimedia Indexing

This paper presents the semantic pathfinder architecture for generic indexing of multimedia archives. The semantic pathfinder extracts semantic concepts from video by exploring different paths through three consecutive analysis steps, which we derive from the observation that produced video is the result of an authoring-driven process. We exploit this authoring metaphor for machine-driven understanding. The pathfinder starts with the content analysis step. In this analysis step, we follow a data-driven approach of indexing semantics. The style analysis step is the second analysis step. Here, we tackle the indexing problem by viewing a video from the perspective of production. Finally, in the context analysis step, we view semantics in context. The virtue of the semantic pathfinder is its ability to learn the best path of analysis steps on a per-concept basis. To show the generality of this novel indexing approach, we develop detectors for a lexicon of 32 concepts and we evaluate the semantic pathfinder against the 2004 NIST TRECVID video retrieval benchmark, using a news archive of 64 hours. Top ranking performance in the semantic concept detection task indicates the merit of the semantic pathfinder for generic indexing of multimedia archives

The distributed ASCI Supercomputer project

The Distributed ASCI Supercomputer (DAS) is a homogeneous wide-area distributed system consisting of four cluster computers at different locations. DAS has been used for research on communication software, parallel languages and programming systems, schedulers, parallel applications, and distributed applications. The paper gives a preview of the most interesting research results obtained so far in the DAS project.

A Learned Lexicon-Driven Paradigm for Interactive Video Retrieval

Effective video retrieval is the result of interplay between interactive query selection, advanced visualization of results, and a goal-oriented human user. Traditional interactive video retrieval approaches emphasize paradigms, such as query-by-keyword and query-by-example, to aid the user in the search for relevant footage. However, recent results in automatic indexing indicate that query-by-concept is becoming a viable resource for interactive retrieval also. We propose in this paper a new video retrieval paradigm. The core of the paradigm is formed by first detecting a large lexicon of semantic concepts. From there, we combine query-by-concept, query-by-example, query-by-keyword, and user interaction into the MediaMill semantic video search engine. To measure the impact of increasing lexicon size on interactive video retrieval performance, we performed two experiments against the 2004 and 2005 NIST TRECVID benchmarks, using lexicons containing 32 and 101 concepts, respectively. The results suggest that from all factors that play a role in interactive retrieval, a large lexicon of semantic concepts matters most. Indeed, by exploiting large lexicons, many video search questions are solvable without using query-by-keyword and query-by-example. In addition, we show that the lexicon-driven search engine outperforms all state-of-the-art video retrieval systems in both TRECVID 2004 and 2005

A software architecture for user transparent parallel image processing

This paper describes a software architecture that allows image processing researchers to develop parallel applications in a transparent manner. The architectures main component is an extensive library of data parallel low level image operations capable of running on homogeneous distributed memory MIMD-style multicomputers. Since the library has an application programming interface identical to that of an existing sequential library, all parallelism is completely hidden from the user.The first part of the paper discusses implementation aspects of the parallel library, and shows how sequential as well as parallel operations are implemented on the basis of so-called parallelizable patterns. A library built in this manner is easily maintainable, as extensive code redundancy is avoided. The second part of the paper describes the application of performance models to ensure efficiency of execution on all target platforms. Experiments show that for a realistic application performance predictions are highly accurate. These results indicate that the core of the architecture forms a powerful basis for automatic parallelization and optimization of a wide range of imaging software.

High-Performance Distributed Video Content Analysis with Parallel-Horus

As the world uses more digital video that requires greater storage space, grid computing is becoming indispensable for urgent problems in multimedia content analysis. Parallel-Horus, a support tool for applications in multimedia grid computing, lets users implement multimedia applications as sequential programs for efficient execution on clusters and grids, based on wide-area multimedia services.

Bootstrapping Visual Categorization With Relevant Negatives

Learning classifiers for many visual concepts are important for image categorization and retrieval. As a classifier tends to misclassify negative examples which are visually similar to positive ones, inclusion of such misclassified and thus relevant negatives should be stressed during learning. User-tagged images are abundant online, but which images are the relevant negatives remains unclear. Sampling negatives at random is the de facto standard in the literature. In this paper, we go beyond random sampling by proposing Negative Bootstrap. Given a visual concept and a few positive examples, the new algorithm iteratively finds relevant negatives. Per iteration, we learn from a small proportion of many user-tagged images, yielding an ensemble of meta classifiers. For efficient classification, we introduce Model Compression such that the classification time is independent of the ensemble size. Compared with the state of the art, we obtain relative gains of 14% and 18% on two present-day benchmarks in terms of mean average precision. For concept search in one million images, model compression reduces the search time from over 20 h to approximately 6 min. The effectiveness and efficiency, without the need of manually labeling any negatives, make negative bootstrap appealing for learning better visual concept classifiers.

MediaMill: exploring news video archives based on learned semantics

In this technical demonstration we showcase the MediaMill system. A search engine that facilitates access to news video archives at a semantic level. The core of the system is an unprecedented lexicon of 100 automatically detected semantic concepts. Based on this lexicon we demonstrate how users can obtain highly relevant retrieval results using query-by-concept. In addition, we show how the lexicon of concepts can be exploited for novel applications using advanced semantic visualizations. Several aspects of the MediaMill system are evaluated as part of our TRECVID 2005 efforts.

Learned lexicon-driven interactive video retrieval

We combine in this paper automatic learning of a large lexicon of semantic concepts with traditional video retrieval methods into a novel approach to narrow the semantic gap. The core of the proposed solution is formed by the automatic detection of an unprecedented lexicon of 101 concepts. From there, we explore the combination of query-by-concept, query-by-example, query-by-keyword, and user interaction into the MediaMill semantic video search engine. We evaluate the search engine against the 2005 NIST TRECVID video retrieval benchmark, using an international broadcast news archive of 85 hours. Top ranking results show that the lexicon-driven search engine is highly effective for interactive video retrieval.

User Transparency: A Fully Sequential Programming Model for Efficient Data Parallel Image Processing

Although many image processing applications are ideally suited for parallel implementation, most researchers in imaging do not benefit from high‐performance computing on a daily basis. Essentially, this is due to the fact that no parallelization tools exist that truly match the image processing researchers frame of reference. As it is unrealistic to expect imaging researchers to become experts in parallel computing, tools must be provided to allow them to develop high‐performance applications in a highly familiar manner. In an attempt to provide such a tool, we have designed a software architecture that allows transparent (i.e. sequential) implementation of data parallel imaging applications for execution on homogeneous distributed memory MIMD‐style multicomputers. This paper presents an extensive overview of the design rationale behind the software architecture, and gives an assessment of the architectures effectiveness in providing significant performance gains. In particular, we describe the implementation and automatic parallelization of three well‐known example applications that contain many fundamental imaging operations: (1) template matching; (2) multi‐baseline stereo vision; and (3) line detection. Based on experimental results we conclude that our software architecture constitutes a powerful and user‐friendly tool for obtaining high performance in many important image processing research areas. Copyright

The ImageNet Shuffle: Reorganized Pre-training for Video Event Detection

This paper strives for video event detection using a representation learned from deep convolutional neural networks. Different from the leading approaches, who all learn from the 1,000 classes defined in the ImageNet Large Scale Visual Recognition Challenge, we investigate how to leverage the complete ImageNet hierarchy for pre-training deep networks. To deal with the problems of over-specific classes and classes with few images, we introduce a bottom-up and top-down approach for reorganization of the ImageNet hierarchy based on all its 21,814 classes and more than 14 million images. Experiments on the TRECVID Multimedia Event Detection 2013 and 2015 datasets show that video representations derived from the layers of a deep neural network pre-trained with our reorganized hierarchy i) improves over standard pre-training, ii) is complementary among different reorganizations, iii) maintains the benefits of fusion with other modalities, and v) leads to state-of-the-art event detection results. The reorganized hierarchies and their derived Caffe models are publicly available at http://tinyurl.com/imagenetshuffle.