Imperial College London

DrKrystianMikolajczyk

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Reader in Computer Vision
 
 
 
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Contact

 

+44 (0)20 7594 6220k.mikolajczyk

 
 
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Location

 

Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
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68 results found

Ramisa A, Yan F, Moreno-Noguer F, Mikolajczyk Ket al., 2018, BreakingNews: Article Annotation by Image and Text Processing, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, Vol: 40, Pages: 1072-1085, ISSN: 0162-8828

JOURNAL ARTICLE

Balntas V, Tang L, Mikolajczyk K, 2018, Binary Online Learned Descriptors, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol: 40, Pages: 555-567, ISSN: 0162-8828

JOURNAL ARTICLE

Koniusz P, Yan F, Gosselin P-H, Mikolajczyk Ket al., 2017, Higher-Order Occurrence Pooling for Bags-of-Words: Visual Concept Detection, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, Vol: 39, Pages: 313-326, ISSN: 0162-8828

JOURNAL ARTICLE

Akin O, Erdem E, Erdem A, Mikolajczyk Ket al., 2016, Deformable part-based tracking by coupled global and local correlation filters, Journal of Visual Communication and Image Representation, Vol: 38, Pages: 763-774, ISSN: 1047-3203

JOURNAL ARTICLE

Chan CH, Yan F, Kittler J, Mikolajczyk Ket al., 2015, Full ranking as local descriptor for visual recognition: A comparison of distance metrics on sn, Pattern Recognition, Vol: 48, Pages: 1328-1336, ISSN: 0031-3203

JOURNAL ARTICLE

Yan F, Kittler J, Windridge D, Christmas W, Mikolajczyk K, Cox S, Huang Qet al., 2014, Automatic annotation of tennis games: An integration of audio, vision, and learning, Image and Vision Computing, Vol: 32, Pages: 896-903, ISSN: 0262-8856

JOURNAL ARTICLE

Bowden R, Collomosse J, Mikolajczyk K, 2014, Guest Editorial: Tracking, Detection and Segmentation, International Journal of Computer Vision, Vol: 110, Pages: 1-1, ISSN: 0920-5691

JOURNAL ARTICLE

Balntas V, Tang L, Mikolajczyk K, 2014, Improving Object Tracking with Voting from False Positive Detections, 2014 22nd International Conference on Pattern Recognition (ICPR), Publisher: IEEE

CONFERENCE PAPER

Akin O, Mikolajczyk K, 2014, Online Learning and Detection with Part-Based, Circulant Structure, 2014 22nd International Conference on Pattern Recognition (ICPR), Publisher: IEEE

CONFERENCE PAPER

Gaur A, Mikolajczyk K, 2014, Ranking Images Based on Aesthetic Qualities, 2014 22nd International Conference on Pattern Recognition (ICPR), Publisher: IEEE

CONFERENCE PAPER

Schubert F, Mikolajczyk K, 2014, Robust Registration and Filtering for Moving Object Detection in Aerial Videos, 2014 22nd International Conference on Pattern Recognition (ICPR), Publisher: IEEE

CONFERENCE PAPER

Yan F, Mikolajczyk K, 2014, Leveraging High Level Visual Information for Matching Images and Captions, Asian Conference on Computer Vision

CONFERENCE PAPER

Schubert F, Mikolajczyk K, 2013, Performance evaluation of image filtering for classification and retrieval, Pages: 485-491

Much research effort in the literature is focused on improving feature extraction methods to boost the performance in various computer vision applications. This is mostly achieved by tailoring feature extraction methods to specific tasks. For instance, for the task of object detection often new features are designed that are even more robust to natural variations of a certain object class and yet discriminative enough to achieve high precision. This focus led to a vast amount of different feature extraction methods with more or less consistent performance across different applications. Instead of fine-tuning or re-designing new features to further increase performance we want to motivate the use of image filters for pre-processing. We therefore present a performance evaluation of numerous existing image enhancement techniques which help to increase performance of already well-known feature extraction methods. We investigate the impact of such image enhancement or filtering techniques on two state-of-the-art image classification and retrieval approaches. For classification we evaluate using a standard Pascal VOC dataset. For retrieval we provide a new challenging dataset. We find that gradient-based interest-point detectors and descriptors such as SIFT or HOG can benefit from enhancement methods and lead to improved performance.

CONFERENCE PAPER

Koniusz P, Yan F, Mikolajczyk K, 2013, Comparison of mid-level feature coding approaches and pooling strategies in visual concept detection, Computer Vision and Image Understanding, Vol: 117, Pages: 479-492, ISSN: 1077-3142

JOURNAL ARTICLE

Boudissa A, Tan J, Kim H, Ishikawa S, Shinomiya T, Mikolajczyk Ket al., 2013, A Global-Local Approach to Saliency Detection, Publisher: Springer Berlin Heidelberg, Pages: 332-337, ISSN: 0302-9743

CONFERENCE PAPER

Schubert F, Mikolajczyk K, 2013, Benchmarking GPU-Based Phase Correlation for Homography-Based Registration of Aerial Imagery, Publisher: Springer Berlin Heidelberg, Pages: 83-90, ISSN: 0302-9743

CONFERENCE PAPER

Tahir M, Yan F, Koniusz P, Awais M, Barnard M, Mikolajczyk K, Kittler Jet al., 2012, A Robust and Scalable Visual Category and Action Recognition System using Kernel Discriminant Analysis with Spectral Regression, IEEE Transactions on Multimedia, ISSN: 1520-9210

JOURNAL ARTICLE

Kalal Z, Mikolajczyk K, Matas J, 2012, Tracking-Learning-Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol: 34, Pages: 1409-1422, ISSN: 0162-8828

JOURNAL ARTICLE

Miksik O, Mikolajczyk K, 2012, Evaluation of local detectors and descriptors for fast feature matching, 21st International Conference on Pattern Recognition, Publisher: IEEE, Pages: 2681-2684, ISSN: 1051-4651

Local feature detectors and descriptors are widely used in many computer vision applications and various methods have been proposed during the past decade. There have been a number of evaluations focused on various aspects of local features, matching accuracy in particular, however there has been no comparisons considering the accuracy and speed trade-offs of recent extractors such as BRIEF, BRISK, ORB, MRRID, MROGH and LIOP. This paper provides a performance evaluation of recent feature detectors and compares their matching precision and speed in randomized kd-trees setup as well as an evaluation of binary descriptors with efficient computation of Hamming distance. © 2012 ICPR Org Committee.

CONFERENCE PAPER

, 2012, British Machine Vision Conference, BMVC 2012, Surrey, UK, September 3-7, 2012, Publisher: BMVA Press

CONFERENCE PAPER

Koniusz P, Mikolajczyk K, 2011, Spatial Coordinate Coding to reduce histogram representations, Dominant Angle and Colour Pyramid Match, 2011 18th IEEE International Conference on Image Processing (ICIP 2011), Publisher: IEEE

CONFERENCE PAPER

Koniusz P, Mikolajczyk K, 2011, Soft assignment of visual words as Linear Coordinate Coding and optimisation of its reconstruction error, 2011 18th IEEE International Conference on Image Processing (ICIP 2011), Publisher: IEEE

CONFERENCE PAPER

Mikolajczyk K, Uemura H, 2011, Action recognition with appearance–motion features and fast search trees, Computer Vision and Image Understanding, Vol: 115, Pages: 426-438, ISSN: 1077-3142

JOURNAL ARTICLE

Hongping Cai, Mikolajczyk K, Matas J, 2011, Learning Linear Discriminant Projections for Dimensionality Reduction of Image Descriptors, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol: 33, Pages: 338-352, ISSN: 0162-8828

JOURNAL ARTICLE

Awais M, Yan F, Mikolajczyk K, Kittler Jet al., 2011, Augmented Kernel Matrix vs classifier fusion for object recognition, BMVC 2011 - Proceedings of the British Machine Vision Conference 2011

Augmented Kernel Matrix (AKM) has recently been proposed to accommodate for the fact that a single training example may have different importance in different feature spaces, in contrast to Multiple Kernel Learning (MKL) that assigns the same weight to all examples in one feature space. However, the AKM approach is limited to small datasets due to its memory requirements. An alternative way to fuse information from different feature channels is classifier fusion (ensemble methods). There is a significant amount of work on linear programming formulations of classifier fusion (CF) in the case of binary classification. In this paper we derive primal and dual of AKM to draw its correspondence with CF. We propose a multiclass extension of binary v-LPBoost, which learns the contribution of each class in each feature channel. Existing approaches of CF promote sparse features combinations, due to regularization based on ℓ1-norm, and lead to a selection of a subset of feature channels, which is not good in case of informative channels. We also generalize existing CF formulations to arbitrary ℓp-norm for binary and multiclass problems which results in more effective use of complementary information. We carry out an extensive comparison and show that the proposed nonlinear CF schemes outperform its sparse counterpart as well as state-of-the-art MKL approaches. © 2011. The copyright of this document resides with its authors.

JOURNAL ARTICLE

Yan F, Mikolajczyk K, Kittler J, 2011, Multiple Kernel Learning via Distance Metric Learning for Interactive Image Retrieval, 10th International Workshop on Multiple Classifier Systems, Publisher: SPRINGER-VERLAG BERLIN, Pages: 147-156, ISSN: 0302-9743

CONFERENCE PAPER

de Campos T, Barnard M, Mikolajczyk K, Kittler J, Yan F, Christmas W, Windridge Det al., 2011, An evaluation of bags-of-words and spatio-temporal shapes for action recognition, 2011 IEEE Workshop on Applications of Computer Vision (WACV), Publisher: IEEE

CONFERENCE PAPER

Awais M, Yan F, Mikolajczyk K, Kittler Jet al., 2011, Two-Stage Augmented Kernel Matrix for Object Recognition, Publisher: Springer Berlin Heidelberg, Pages: 137-146, ISSN: 0302-9743

CONFERENCE PAPER

Awais M, Yan F, Mikolajczyk K, Kittler Jet al., 2011, Novel Fusion Methods for Pattern Recognition, Publisher: Springer Berlin Heidelberg, Pages: 140-155, ISSN: 0302-9743

CONFERENCE PAPER

Awais M, Yan F, Mikolajczyk K, Kittler Jet al., 2011, Augmented Kernel Matrix vs Classifier Fusion for Object Recognition, British Machine Vision Conference 2011, Publisher: British Machine Vision Association

CONFERENCE PAPER

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