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Selective Search for Object Recognition
This paper introduces selective search which combines the strength of both an exhaustive search and segmentation, and shows that its selective search enables the use of the powerful Bag-of-Words model for recognition.
Content-Based Image Retrieval at the End of the Early Years
The working conditions of content-based retrieval: patterns of use, types of pictures, the role of semantics, and the sensory gap are discussed, as well as aspects of system engineering: databases, system architecture, and evaluation.
The Amsterdam Library of Object Images
In order to capture the sensory variation in object recordings, this work systematically varied viewing angle, illumination angle, and illumination color for each object, and additionally captured wide-baseline stereo images.
Visual Tracking: An Experimental Survey
It is demonstrated that trackers can be evaluated objectively by survival curves, Kaplan Meier statistics, and Grubs testing, and it is found that in the evaluation practice the F-score is as effective as the object tracking accuracy (OTA) score.
Siamese Instance Search for Tracking
It turns out that the learned matching function is so powerful that a simple tracker built upon it, coined Siamese INstance search Tracker, SINT, suffices to reach state-of-the-art performance.
The Sixth Visual Object Tracking VOT2018 Challenge Results
The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty trackers are presented; many are
Segmentation as selective search for object recognition
This work adapt segmentation as a selective search by reconsidering segmentation to generate many approximate locations over few and precise object delineations because an object whose location is never generated can not be recognised and appearance and immediate nearby context are most effective for object recognition.
Visual Word Ambiguity
It is demonstrated that explicitly modeling visual word assignment ambiguity improves classification performance compared to the hard assignment of the traditional codebook model, and the proposed model performs consistently.
The challenge problem for automated detection of 101 semantic concepts in multimedia
We introduce the challenge problem for generic video indexing to gain insight in intermediate steps that affect performance of multimedia analysis methods, while at the same time fostering
Kernel Codebooks for Scene Categorization
It is shown that allowing a degree of ambiguity in assigning codewords improves categorization performance for three state-of-the-art datasets.