Transductive Reliability Research Articles

A novel approach for multi-cue feature fusion for robust object tracking

Object tracking is a significant problem of computer vision due to challenging environmental variations. Single cue appearance model is not sufficient to handle the variations. To this end, we propose a multi-cue tracking framework in which complementary cues namely, LBP and HOG were exploited to develop a robust appearance model. The proposed feature fusion captures the high-level relationship between the features and diminishes the low-level relationship. Transductive reliability is also integrated at each frame to make tracker adaptive with the changing environment. In addition, K-Means based classifier creates clear and concise boundary between positive and negative fragments which are further used to update the reference dictionary. This adaptation strategy prevents the erroneous updation of the proposed tracker during background clutters, occlusion, and fast motion. Qualitative and quantitative analysis on challenging video sequences from OTB-100 dataset, VOT dataset and UAV123 reveal that the proposed tracker performs favorably against 13 others state-of-the-art trackers.

Robust object tracking with crow search optimized multi-cue particle filter

Particle filter is used extensively for estimation of target nonlinear and non-Gaussian state. However, its performance suffers due to its inherent problem of sample degeneracy and impoverishment. In order to address this, we propose a novel resampling method based upon crow search optimization to overcome low performing particles detected as the outlier. Proposed outlier detection mechanism with transductive reliability achieves faster convergence of the proposed PF tracking framework. In addition, we present an adaptive fusion model to integrate multi-cue extracted for each evaluated particle. Automatic boosting and suppression of particles using the proposed fusion model not only enhance the performance of the resampling method but also achieve optimal state estimation. Performance of the proposed tracker has been evaluated over benchmark video sequences and compared with state-of-the-art solutions. Qualitative and quantitative results reveal that the proposed tracker not only outperforms existing solutions but also efficiently handles various tracking challenges. On average of the outcome, we achieve CLE of 10.99 and F measure of 0.683.

Unified Graph-Based Multicue Feature Fusion for Robust Visual Tracking.

Visual tracking is a complex problem due to unconstrained appearance variations and a dynamic environment. The extraction of complementary information from the object environment via multiple features and adaption to the target's appearance variations are the key problems of this paper. To this end, we propose a robust object tracking framework based on the unified graph fusion (UGF) of multicue to adapt to the object's appearance. The proposed cross-diffusion of sparse and dense features not only suppresses the individual feature deficiencies but also extracts the complementary information from multicue. This iterative process builds robust unified features which are invariant to object deformations, fast motion, and occlusion. Robustness of the unified feature also enables the random forest classifier to precisely distinguish the foreground from the background, adding resilience to background clutter. In addition, we present a novel kernel-based adaptation strategy using outlier detection and a transductive reliability metric. The adaptation strategy updates the appearance model to accommodate variations in scale, illumination, and rotation. Both qualitative and quantitative analyses on benchmark video sequences from OTB-50, OTB-100, VOT2017/18, and UAV123 show that the proposed UGF tracker performs favorably against 18 other state-of-the-art trackers under various object tracking challenges.

Quality assessment of individual classifications in machine learning and data mining

Although in the past machine learning algorithms have been successfully used in many problems, their serious practical use is affected by the fact that often they cannot produce reliable and unbiased assessments of their predictions' quality. In last few years, several approaches for estimating reliability or confidence of individual classifiers have emerged, many of them building upon the algorithmic theory of randomness, such as (historically ordered) transduction-based confidence estimation, typicalness-based confidence estimation, and transductive reliability estimation. Unfortunately, they all have weaknesses: either they are tightly bound with particular learning algorithms, or the interpretation of reliability estimations is not always consistent with statistical confidence levels. In the paper we describe typicalness and transductive reliability estimation frameworks and propose a joint approach that compensates the above-mentioned weaknesses by integrating typicalness-based confidence estimation and transductive reliability estimation into a joint confidence machine. The resulting confidence machine produces confidence values in the statistical sense. We perform series of tests with several different machine learning algorithms in several problem domains. We compare our results with that of a proprietary method as well as with kernel density estimation. We show that the proposed method performs as well as proprietary methods and significantly outperforms density estimation methods.

Transductive reliability estimation for medical diagnosis

In the past decades, machine learning (ML) tools have been successfully used in several medical diagnostic problems. While they often significantly outperform expert physicians (in terms of diagnostic accuracy, sensitivity, and specificity), they are mostly not being used in practice. One reason for this is that it is difficult to obtain an unbiased estimation of diagnose’s reliability. We discuss how reliability of diagnoses is assessed in medical decision-making and propose a general framework for reliability estimation in machine learning, based on transductive inference. We compare our approach with a usual (machine learning) probabilistic approach as well as with classical stepwise diagnostic process where reliability of diagnose is presented as its post-test probability. The proposed transductive approach is evaluated on several medical datasets from the University of California (UCI) repository as well as on a practical problem of clinical diagnosis of the coronary artery disease (CAD). In all cases, significant improvements over existing techniques are achieved.