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Development of content-based image retrieval (CBIR) techniques has suffered from the lack of standardized ways for describing visual image content. Luckily, the MPEG-7 international standard is now emerging as both a general framework for content description and a collection of specific agreed-upon content descriptors. We have developed a neural,(More)
We have developed a novel system for content-based image retrieval in large, unannotated databases. The system is called PicSOM, and it is based on tree structured self-organizing maps (TS-SOMs). Given a set of reference images, PicSOM is able to retrieve another set of images which are similar to the given ones. Each TS-SOM is formed with a di€erent image(More)
The PASCAL Visual Object Classes Challenge ran from February to March 2005. The goal of the challenge was to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects). Four object classes were selected: motorbikes , bicycles, cars and people. Twelve teams entered the challenge. In this chapter we provide(More)
Content-based image retrieval (CBIR) addresses the problem of assisting a user to retrieve images from unannotated databases, based on features that can be automatically derived from the images. Today, there exists several CBIR systems based on different methods. Only few attemps to benchmark these have been made, although the usefulness of benchmarking is(More)
—The recent development of large-scale multimedia concept ontologies has provided a new momentum for research in the semantic analysis of multimedia repositories. Different methods for generic concept detection have been extensively studied, but the question of how to exploit the structure of a multimedia ontology and existing inter-concept relations has(More)
In content-based image retrieval (CBIR), the images in a database are indexed on the basis of low-level statistical features that can be automatically derived from the images. Due to the semantic gap, the performance of CBIR systems often remains quite modest especially on broad image domains. One method for improving the results is to incorporate automatic(More)
This paper describes how low-level statistical visual features can be analyzed in our content-based image retrieval system named PicSOM. The low-level visual features used in the system are all statistical by nature. They include average color, color moments, contrast-type textural feature, and edge histogram and Fourier transform based shape features.(More)
Our experiments in TRECVID 2008 include participation in the high-level feature extraction, automatic search, video summariza-tion, and video copy detection tasks, using a common system framework. In the high-level feature extraction task, we extended our last year's experiments, which were based on SOM-based semantic concept modeling followed by a(More)
In this paper we present a robust motion recognition framework for both motion capture and RGB-D sensor data. We extract four different types of features and apply a temporal difference operation to form the final feature vector for each frame in the motion sequences. The frames are classified with the extreme learning machine, and the final class of an(More)
In this paper, we consider the automatic identification of video shots that are relevant to a given semantic concept from large video databases. We apply a method of representing semantic concepts as class models on a set of parallel Self-Organizing Maps trained with multimodal low-level features. The presented experiments were conducted using a set of 170(More)