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This paper addresses the problem of generating possible object locations for use in object recognition. We introduce selective search which combines the strength of both an exhaustive search and segmentation. Like segmentation, we use the image structure to guide our sampling process. Like exhaustive search, we aim to capture all possible object locations.(More)
For object recognition, the current state-of-the-art is based on exhaustive search. However, to enable the use of more expensive features and classifiers and thereby progress beyond the state-of-the-art, a selective search strategy is needed. Therefore, we adapt segmentation as a selective search by reconsidering segmentation: We propose to generate many(More)
—As datasets grow increasingly large in content-based image and video retrieval, computational efficiency of concept classification is important. This paper reviews techniques to accelerate concept classification, where we show the trade-off between computational efficiency and accuracy. As a basis, we use the Bag-of-20 classes all images that are currently(More)
In this paper we describe our TRECVID 2008 video retrieval experiments. The MediaMill team participated in three tasks: concept detection, automatic search, and interactive search. Rather than continuing to increase the number of concept detectors available for retrieval, our TRECVID 2008 experiments focus on increasing the robustness of a small set of(More)
The current trend in image analysis and multimedia is to use information extracted from text and text processing techniques to help vision-related tasks, such as automated image annotation and generating semantically rich descriptions of images. In this work, we claim that image analysis techniques can "return the favor" to the text processing community and(More)
We start from the state-of-the-art Bag of Words pipeline that in the 2008 benchmarks of TRECvid and PASCAL yielded the best performance scores. We have contributed to that pipeline, which now forms the basis to compare various fast alternatives for all of its components: (<i>i</i>) For descriptor extraction we propose a fast algorithm to densely sample SIFT(More)
This paper discusses the question: Can we improve the recognition of objects by using their spatial context? We start from Bag-of-Words models and use the Pascal 2007 dataset. We use the rough object bounding boxes that come with this dataset to investigate the fundamental gain context can bring. Our main contributions are: (I) The result of Zhang et al. in(More)
The visual extent of an object reaches beyond the object itself. This is a long standing fact in psychology and is reflected in image retrieval techniques which aggregate statistics from the whole image in order to identify the object within. However, it is unclear to what degree and how the visual extent of an object affects classification performance. In(More)
The aim of this paper is threefold: (a) to introduce a dataset for the recognition of events and sub-events in photographs taken by common users; (b) to propose event-based classification to achieve a more accurate labeling of event-related photo collections; (c) to use time clustering information to improve the sub-event recognition in an efficient Bag of(More)
When artists express their feelings through the artworks they create, it is believed that the resulting works transform into objects with &#8220;emotions&#8221; capable of conveying the artists' mood to the audience. There is little to no dispute about this belief: Regardless of the artwork, genre, time, and origin of creation, people from different(More)