Bag-Level Aggregation for Multiple Instance Active Learning in Instance Classification Problems

A growing number of applications, e.g. video surveillance and medical image analysis, require training recognition systems from large amounts of weakly annotated data while some targeted interactions with a domain expert are allowed to improve the training process. In such cases, active learning (AL) can reduce labeling costs for training a classifier by querying the expert to provide the labels of most informative instances. This paper focuses on AL methods for instance classification problems in multiple instance learning (MIL), where data is arranged into sets, called bags, that are weakly labeled. Most AL methods focus on single instance learning problems. These methods are not suitable for MIL problems because they cannot account for the bag structure of data. In this paper, new methods for bag-level aggregation of instance informativeness are proposed for multiple instance active learning (MIAL). The \textit{aggregated informativeness} method identifies the most informative instances based on classifier uncertainty, and queries bags incorporating the most information. The other proposed method, called \textit{cluster-based aggregative sampling}, clusters data hierarchically in the instance space. The informativeness of instances is assessed by considering bag labels, inferred instance labels, and the proportion of labels that remain to be discovered in clusters. Both proposed methods significantly outperform reference methods in extensive experiments using benchmark data from several application domains. Results indicate that using an appropriate strategy to address MIAL problems yields a significant reduction in the number of queries needed to achieve the same level of performance as single instance AL methods

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

Image Retrieval with Relevance Feedback using SVM Active Learning

In content-based image retrieval, relevant feedback is studied extensively to narrow the gap between low-level image feature and high-level semantic concept. In general, relevance feedback aims to improve the retrieval performance by learning with user's judgements on the retrieval results. Despite widespread interest, but feedback related technologies are often faced with a few limitations. One of the most obvious limitations is often requiring the user to repeat a number of steps before obtaining the improved search results. This makes the process inefficient and tedious search for the online applications. In this paper, a effective feedback related scheme for content-based image retrieval is proposed. First, a decision boundary is learned via Support Vector Machine to filter the images in the database. Then, a ranking function for selecting the most informative samples will be calculated by defining a novel criterion that considers both the scores of Support Vector Machine function and similaritymetric between the "ideal query" and the images in the database. The experimental results on standard datasets have showed the effectiveness of the proposed method

Semisupervised SVM batch mode active learning with applications to image retrieval

Ministry of Education, Singapore under its Academic Research Funding Tier