Information-Theoretic Active Learning for Content-Based Image Retrieval

We propose Information-Theoretic Active Learning (ITAL), a novel batch-mode active learning method for binary classification, and apply it for acquiring meaningful user feedback in the context of content-based image retrieval. Instead of combining different heuristics such as uncertainty, diversity, or density, our method is based on maximizing the mutual information between the predicted relevance of the images and the expected user feedback regarding the selected batch. We propose suitable approximations to this computationally demanding problem and also integrate an explicit model of user behavior that accounts for possible incorrect labels and unnameable instances. Furthermore, our approach does not only take the structure of the data but also the expected model output change caused by the user feedback into account. In contrast to other methods, ITAL turns out to be highly flexible and provides state-of-the-art performance across various datasets, such as MIRFLICKR and ImageNet.Comment: GCPR 2018 paper (14 pages text + 2 pages references + 6 pages appendix

Spectra of magnetic perturbations triggered by pellets in JET plasmas

Aiming at investigating edge localised mode (ELM) pacing for future application on ITER, experiments have been conducted on JET injecting pellets in different plasma configurations, including high confinement regimes with type-I and type-III ELMs, low confinement regimes and Ohmically heated plasmas. The magnetic perturbations spectra and the toroidal mode number, n, of triggered events are compared with those of spontaneous ELMs using a wavelet analysis to provide good time resolution of short-lived coherent modes. It is found that—in all these configurations—triggered events have a coherent mode structure, indicating that pellets can trigger an MHD event basically in every background plasma. Two components have been found in the magnetic perturbations induced by pellets, with distinct frequencies and toroidal mode numbers. In high confinement regimes triggered events have similarities with spontaneous ELMs: both are seen to start from low toroidal mode numbers, then the maximum measured n increases up to about 10 within 0.3 ms before the ELM burst

Theory and experiment of differential acoustic resonance spectroscopy

Abstract Recent advances in Differential Acoustic Resonance Spectroscopy (DARS) techniques have given rise to applications in the field of poromechanics. We report on the experimental demonstration of bulk modulus measurements on poroelastic samples at sonic frequencies (1 kHz) with DARS. Normal mode perturbation is due to scattering of a foreign object (i.e., a rock sample) within an otherwise fluid-filled resonator. The perturbation theory on an elastic object determines its bulk modulus (inverse compressibility). The experimental bulk modulus of medium-to high-permeability (>10 mD) poroelastic samples is in agreement with predictions from quasi-static loading of a porous sphere using the Biot theory. This result demonstrates that pore fluid flow governs the dominant relaxation process of the rock during compression. For low-permeability samples (<10 mD), pressure equilibration via slow wave diffusion is limited, and only qualitative agreement is found between the upper bound (Gassmann undrained modulus) and the lower bound (volume-weighted compressibilities of the two constituents). DARS experiments, in conjunction with the poroelastic theory presented here, allow one to infer such rock physical properties as the effective bulk modulus at sonic frequencies

Long-Term Visual Object Tracking Benchmark

We propose a new long video dataset (called Track Long and Prosper - TLP) and benchmark for single object tracking. The dataset consists of 50 HD videos from real world scenarios, encompassing a duration of over 400 minutes (676K frames), making it more than 20 folds larger in average duration per sequence and more than 8 folds larger in terms of total covered duration, as compared to existing generic datasets for visual tracking. The proposed dataset paves a way to suitably assess long term tracking performance and train better deep learning architectures (avoiding/reducing augmentation, which may not reflect real world behaviour). We benchmark the dataset on 17 state of the art trackers and rank them according to tracking accuracy and run time speeds. We further present thorough qualitative and quantitative evaluation highlighting the importance of long term aspect of tracking. Our most interesting observations are (a) existing short sequence benchmarks fail to bring out the inherent differences in tracking algorithms which widen up while tracking on long sequences and (b) the accuracy of trackers abruptly drops on challenging long sequences, suggesting the potential need of research efforts in the direction of long-term tracking.Comment: ACCV 2018 (Oral

Prosemantic features for content-based image retrieval

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-18449-9_8Revised Selected Papers of 7th International Workshop, AMR 2009, Madrid, Spain, September 24-25, 2009We present here, an image description approach based on prosemantic features. The images are represented by a set of low-level features related to their structure and color distribution. Those descriptions are fed to a battery of image classifiers trained to evaluate the membership of the images with respect to a set of 14 overlapping classes. Prosemantic features are obtained by packing together the scores. To verify the effectiveness of the approach, we designed a target search experiment in which both low-level and prosemantic features are embedded into a content-based image retrieval system exploiting relevance feedback. The experiments show that the use of prosemantic features allows for a more successful and quick retrieval of the query images

Learning Tversky Similarity

In this paper, we advocate Tversky's ratio model as an appropriate basis for computational approaches to semantic similarity, that is, the comparison of objects such as images in a semantically meaningful way. We consider the problem of learning Tversky similarity measures from suitable training data indicating whether two objects tend to be similar or dissimilar. Experimentally, we evaluate our approach to similarity learning on two image datasets, showing that is performs very well compared to existing methods

Content-Based Image Retrieval of Skin Lesions by Evolutionary Feature Synthesis

Abstract. This paper gives an example of evolved features that improve image retrieval performance. A content-based image retrieval system for skin lesion images is presented. The aim is to support decision making by retrieving and displaying relevant past cases visually similar to the one under examination. Skin lesions of five common classes, including two non-melanoma cancer types, are used. Colour and texture features are extracted from lesions. Evolutionary algorithms are used to create composite features that optimise a similarity matching function. Experiments on our database of 533 images are performed and results are compared to those obtained using simple features. The use of the evolved composite features improves the precision by about 7%.