Evaluation of Active Learning Strategies for Video Indexing

International audienceIn this paper, we compare active learning strategies for indexing concepts in video shots. Active learning is simulated using subsets of a fully annotated dataset instead of actually calling for user intervention. Training is done using the collaborative annotation of 39 concepts of the TRECVID 2005 campaign. Performance is measured on the 20 concepts selected for the TRECVID 2006 concept detection task. The simulation allows exploring the effect of several parameters: the strategy, the annotated fraction of the dataset, the number of iterations and the relative difficulty of concepts

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

Development of Integrated Economic Learning Materials Entrepreneurship Values for High School

This research objective is to develop integrated economic teaching in entrepreneurial values and find out eligibility for high school X-IPS1 students. They were using research and development (R&D) methods. The stages of the research include the initial research and information collecting, planning, develop preliminary form a product, preliminary field testing, leading product, operational product revision, and final product revision. The research Subjects used in this study include two validators, namely learning media experts, 37 class X-IPS1 students, and one economics teacher.  The result achieved in the development of integrated economic teaching materials are entrepreneurial values, namely creative value, independence values, leadership values, risk-bearing values, and action-oriented values that were developed were categorized as useful from the aspects of content worthiness, language presentation was more than 75%. So the result of these studies can be entrepreneurial values are appropriate for use in learning in High School Class. &nbsp

Simulating the Future of Concept-Based Video Retrieval under Improved Detector Performance

In this paper we address the following important questions for concept-based video retrieval: (1) What is the impact of detector performance on the performance of concept-based retrieval engines, and (2) will these engines be applicable to real-life search tasks if detector performance improves in the future? We use Monte Carlo simulations to answer these questions. To generate the simulation input, we propose to use a probabilistic model of two Gaussians for the confidence scores that concept detectors emit. Modifying the model's parameters affects the detector performance and the search performance. We study the relation between these two performances on two video collections. For detectors with similar discriminative power and a concept vocabulary of around 100 concepts, the simulation reveals that in order to achieve a search performance of 0.20 mean average precision (MAP) -- which is considered sufficient performance for real-life applications -- one needs detectors with at least 0.60 MAP. We also find that, given our simulation model and low detector performance, MAP is not always a good evaluation measure for concept detectors since it is not strongly correlated with the search performance

Pengembangan Media Pembelajaran Interaktif Berbasis Video Tutorial Di Sekolah Menengah Kejuruan

Abstract: The rapid exchange of information through multimedia throughout the world helps the growth of humans’ knowledge and, at the same time, requires redefinition of learning methods and media, especially in the field of vocational education. The purpose of this research was to develop an interactive learning media in the form of video tutorial in increasing the learning effectiveness in the subject of audio processing techniques at class XII in multimedia competence in one of the State Vocational Schools in Parepare. Research and Development method was used in this study. The stages of research included the planning, design and development stages. The subjects engaged in this research were two validators namely the experts of learning media and learning material, the students of class XII multimedia with the total of 18 persons, and one teacher who taught audio processing techniques. The results achieved in the development of interactive media showed that the developed video tutorial media had been valid based on the assessment of both learning media and learning material experts. The tests conducted on individuals, small group trials, and the subject teacher’s responses indicated that an interactive media in the form of video tutorial was effective and appropriate to the users’ needs.    Keywords: Research and development, Video Tutorial, Interactive media, Multimedia, Camtasia Studio  Abstrak: Pertukaran informasi yang cepat melalui multimedia di seluruh dunia membantu pertumbuhan pengetahuan manusia dan, pada saat yang sama, membutuhkan pendefinisian ulang metode dan media pembelajaran, terutama di bidang pendidikan kejuruan. Tujuan penelitian yaitu untuk mengembangkan media interaktif berupa video tutorial pembelajaran dalam meningkatkan efektivitas pembelajaran pada mata pelajaran teknik pengolahan audio kelas XII kompetensi keahlian multimedia di salah satu SMK Negeri di Parepare. Penelitian ini merupakan penelitian dan pengembangan (Research and Development). Tahapan penelitian meliputi tahap perencanaan, desain dan pengembangan. Subjek penelitian yang digunakan pada studi ini meliputi dua orang validator yaitu ahli media pembelajaran dan ahli materi pembelajaran, siswa kelas XII multimedia yang berjumlah 18 orang dan satu orang guru mata pelajaran teknik pengolahan audio. Hasil yang dicapai dalam pengembangan media interaktif menunjukkan media vidio tutorial yang dikembangkan telah valid berdasarkan penilaian dari ahli media pembelajaran dan ahli materi. Uji coba yang dilakukan kepada perorangan, uji coba kelompok kecil, dan tanggapan guru mata pelajaran menunjukkan bahwa media interaktif berupa video tutorial pembelajaran yang dihasilkan efektif dan sesuai kebutuhan pengguna.  Keywords: Pengembangan, Video tutorial, Media Interaktif, Multimedia, Camtasia Studi

Incremental Multiple Classifier Active Learning for Concept Indexing in Images and Videos

Regular Papers: Multimedia Indexing and MiningInternational audienceActive learning with multiple classifiers has shown good performance for concept indexing in images or video shots in the case of highly imbalanced data. It involves however a large number of computations. In this paper, we propose a new incremental active learning algorithm based on multiple SVM for image and video annotation. The experimental result show that the best performance (MAP) is reached when 15-30% of the corpus is annotated and the new method can achieve almost the same precision while saving 50 to 63% of the computation time

Visually-Enabled Active Deep Learning for (Geo) Text and Image Classification: A Review

This paper investigates recent research on active learning for (geo) text and image classification, with an emphasis on methods that combine visual analytics and/or deep learning. Deep learning has attracted substantial attention across many domains of science and practice, because it can find intricate patterns in big data; but successful application of the methods requires a big set of labeled data. Active learning, which has the potential to address the data labeling challenge, has already had success in geospatial applications such as trajectory classification from movement data and (geo) text and image classification. This review is intended to be particularly relevant for extension of these methods to GISience, to support work in domains such as geographic information retrieval from text and image repositories, interpretation of spatial language, and related geo-semantics challenges. Specifically, to provide a structure for leveraging recent advances, we group the relevant work into five categories: active learning, visual analytics, active learning with visual analytics, active deep learning, plus GIScience and Remote Sensing (RS) using active learning and active deep learning. Each category is exemplified by recent influential work. Based on this framing and our systematic review of key research, we then discuss some of the main challenges of integrating active learning with visual analytics and deep learning, and point out research opportunities from technical and application perspectives-for application-based opportunities, with emphasis on those that address big data with geospatial components

Active Learning for Text Classification

Text classification approaches are used extensively to solve real-world challenges. The success or failure of text classification systems hangs on the datasets used to train them, without a good dataset it is impossible to build a quality system. This thesis examines the applicability of active learning in text classification for the rapid and economical creation of labelled training data. Four main contributions are made in this thesis. First, we present two novel selection strategies to choose the most informative examples for manually labelling. One is an approach using an advanced aggregated confidence measurement instead of the direct output of classifiers to measure the confidence of the prediction and choose the examples with least confidence for querying. The other is a simple but effective exploration guided active learning selection strategy which uses only the notions of density and diversity, based on similarity, in its selection strategy. Second, we propose new methods of using deterministic clustering algorithms to help bootstrap the active learning process. We first illustrate the problems of using non-deterministic clustering for selecting initial training sets, showing how non-deterministic clustering methods can result in inconsistent behaviour in the active learning process. We then compare various deterministic clustering techniques and commonly used non-deterministic ones, and show that deterministic clustering algorithms are as good as non-deterministic clustering algorithms at selecting initial training examples for the active learning process. More importantly, we show that the use of deterministic approaches stabilises the active learning process. Our third direction is in the area of visualising the active learning process. We demonstrate the use of an existing visualisation technique in understanding active learning selection strategies to show that a better understanding of selection strategies can be achieved with the help of visualisation techniques. Finally, to evaluate the practicality and usefulness of active learning as a general dataset labelling methodology, it is desirable that actively labelled dataset can be reused more widely instead of being only limited to some particular classifier. We compare the reusability of popular active learning methods for text classification and identify the best classifiers to use in active learning for text classification. This thesis is concerned using active learning methods to label large unlabelled textual datasets. Our domain of interest is text classification, but most of the methods proposed are quite general and so are applicable to other domains having large collections of data with high dimensionality

Evaluation of active learning strategies for video indexing

In this paper, we compare active learning strategies for indexing concepts in video shots. Active learning is simulated using subsets of a fully annotated dataset instead of actually calling for user intervention. Training is done using the collaborative annotation of 39 concepts of the TRECVID 2005 campaign. Performance is measured on the 20 concepts selected for the TRECVID 2006 concept detection task. The simulation allows exploring the effect of several parameters: the strategy, the annotated fraction of the dataset, the number of iterations and the relative difficulty of concepts. Three strategies were compared. The first two respectively select the most probable and the most uncertain samples. The third one is a random choice. For easy concepts, the “most probable ” strategy is the best one when less than 15% of the dataset is annotated and the “most uncertain ” strategy is the best one when 15 % or more of the dataset is annotated. The “most probable ” and “most uncertain ” strategies are roughly equivalent for moderately difficult and difficult concepts. In all cases, the maximum performance is reached when 12 to 15 % of the whole dataset is annotated. 1