No Spare Parts: Sharing Part Detectors for Image Categorization

This work aims for image categorization using a representation of distinctive parts. Different from existing part-based work, we argue that parts are naturally shared between image categories and should be modeled as such. We motivate our approach with a quantitative and qualitative analysis by backtracking where selected parts come from. Our analysis shows that in addition to the category parts defining the class, the parts coming from the background context and parts from other image categories improve categorization performance. Part selection should not be done separately for each category, but instead be shared and optimized over all categories. To incorporate part sharing between categories, we present an algorithm based on AdaBoost to jointly optimize part sharing and selection, as well as fusion with the global image representation. We achieve results competitive to the state-of-the-art on object, scene, and action categories, further improving over deep convolutional neural networks

Discriminative Probabilistic Pattern Mining using Graph for Electronic Health Records

학위논문(석사)--서울대학교 대학원 :공과대학 컴퓨터공학부,2019. 8. 김선.전자의료기록(Electronic Health Records)의 임상 노트에는 환자의 병력에 대한 유용한 정보가 많이 포함되어 있다. 그러나 임상 노트는 체계화되지 않은 데이터이며 그 양은 나날이 증가하고 있다. 따라서 임상 노트를 그룹화하고 분류하기 위한 신뢰할 수 있는 데이터 마이닝 기술이 필요하다. 기존의 데이터 마이닝 기술은 키워드의 빈도를 기반으로 생성된 빈발 패턴(frequent patterns)을 이용하여 그룹 분류 작업(classification)을 수행한다. 하지만 이러한 빈발 패턴은 전자의료기록의 임상 노트와 같이 복잡한 데이터의 분류를 위해 필요한 충분히 강력하고 명확하게 구별되는 특징을 갖고 있지 않다. 또한 빈발 패턴 기반 기술은 대규모 전자의료기록 데이터에 적용될 때 확장성과 계산 비용의 문제에 직면한다. 따라서 본 연구에서는 이러한 문제점을 해결하기 위해 확률적 판별 패턴 마이닝(discriminative probabilistic pattern mining) 알고리즘을 소개한다. 확률적 판별 패턴 마이닝 알고리즘에서는 전자의료기록의 임상 노트를 분류하기 위해 그래프 구조를 도입하여 빈발 패턴의 부분 그래프를 생성하게 된다. 본 연구에서는 판별력을 높이기 위해 개별 키워드를 사용하는 대신 이진 특성 조합에서의 동시 출현(co-occurrence)을 사용하여 임상 노트 분류를 위한 빈발 패턴 그래프를 구성한다. 각각의 동시 출현은 판별력(discriminative power)에 따른 log-odds 값으로 그 가중치를 갖는다. 임상 노트의 본질을 반영하는 그래프를 찾기 위해 확률적 판별 부분 그래프 검색을 수행하며 그래프의 허브(hub) 노드에서 시작하여 동적 프로그래밍(dynamic programming)을 사용하여 경로를 찾는다. 이러한 방법으로 검색한 빈발 부분 그래프를 이용하여 전자의료기록의 임상 노트에 대한 분류 작업을 수행하게 된다.Electronic Health Records (EHR) contains plenty of useful information about patients medical history. However, EHR is highly unstructured data and amount of it is growing continuously, that is why there is a need in a reliable data mining technique to group and categorize clinical notes. Although, many existing data mining techniques for group classification use frequent patterns generated based on frequencies of keywords, these patterns do not possess strong enough distinguishing characteristics to show the difference between datasets to classify complex data such as clinical notes in EHR. Also, these techniques encounter scalability and computational cost problems when used on large EHR dataset. To address these issues, we introduce discriminative probabilistic pattern mining algorithm that uses a graph (DPPMG) to generate the subgraphs of frequent patterns for classification in electronic health records. We use co-occurrence, a combination of binary features, which is more discriminative than individual keywords to construct discriminative probabilistic frequent patterns graph for clinical notes classification. Each co-occurrence has a weight of log-odds score that is associated with its discriminative power. The graph, which reflects the essence of clinical notes is searched to find discriminative probabilistic frequent subgraphs. To discover the discriminative frequent subgraphs, we start from a hub node in the graph and use dynamic programming to find a path. The discriminative probabilistic frequent subgraphs discovered by this approach are later used to classify clinical notes of electronic health records.Chapter 1 Introduction and Motivation 1 Chapter 2 Background 4 2.1 Frequent Pattern Based Classification 4 2.2 Discriminative Pattern Mining 5 2.3 Electronic Health Records 6 Chapter 3 Related Work 8 Chapter 4 Overview and Design 10 Chapter 5 Implementation 12 5.1 Dataset 12 5.2 Keyword Extraction and Filtering 15 5.3 Co-occurrence Generation and Graph Construction 16 5.4 Dynamic Programming to Discover Optimal Path 17 Chapter 6 Results and Evaluation 20 6.1 Choosing Starting Hub Node 20 6.2 Qualitative Analysis 22 6.3 Discriminative Power of the Probabilistic Frequent Patterns 24 Chapter 7 Conclusion 26 Bibliography 28 요약 33Maste

Discovering Latent Clusters from Geotagged Beach Images

Abstract. This paper studies the problem of estimating geographical locations of images. To build reliable geographical estimators, an impor-tant question is to find distinguishable geographical clusters in the world. Those clusters cover general geographical regions and are not limited to landmarks. The geographical clusters provide more training samples and hence lead to better recognition accuracy. Previous approaches build geographical clusters using heuristics or arbitrary map grids, and can-not guarantee the effectiveness of the geographical clusters. This paper develops a new framework for geographical cluster estimation, and em-ploys latent variables to estimate the geographical clusters. To solve this problem, this paper employs the recent progress in object detection, and builds an efficient solver to find the latent clusters. The results on beach datasets validate the success of our method.

Mining Mid-level Features for Image Classification

International audienceMid-level or semi-local features learnt using class-level information are potentially more distinctive than the traditional low-level local features constructed in a purely bottom-up fashion. At the same time they preserve some of the robustness properties with respect to occlusions and image clutter. In this paper we propose a new and effective scheme for extracting mid-level features for image classification, based on relevant pattern mining. In par- ticular, we mine relevant patterns of local compositions of densely sampled low-level features. We refer to the new set of obtained patterns as Frequent Local Histograms or FLHs. During this process, we pay special attention to keeping all the local histogram information and to selecting the most relevant reduced set of FLH patterns for classification. The careful choice of the visual primitives and an extension to exploit both local and global spatial information allow us to build powerful bag-of-FLH-based image representations. We show that these bag-of-FLHs are more discriminative than traditional bag-of-words and yield state-of-the-art results on various image classification benchmarks, including Pascal VOC

Advanced machine learning approaches for target detection, tracking and recognition

This dissertation addresses the key technical components of an Automatic Target Recognition (ATR) system namely: target detection, tracking, learning and recognition. Novel solutions are proposed for each component of the ATR system based on several new advances in the field of computer vision and machine learning. Firstly, we introduce a simple and elegant feature, RelCom, and a boosted feature selection method to achieve a very low computational complexity target detector. Secondly, we present a particle filter based target tracking algorithm that uses a quad histogram based appearance model along with online feature selection. Further, we improve the tracking performance by means of online appearance learning where appearance learning is cast as an Adaptive Kalman filtering (AKF) problem which we formulate using both covariance matching and, for the first time in a visual tracking application, the recent autocovariance least-squares (ALS) method. Then, we introduce an integrated tracking and recognition system that uses two generative models to accommodate the pose variations and maneuverability of different ground targets. Specifically, a tensor-based generative model is used for multi-view target representation that can synthesize unseen poses, and can be trained from a small set of signatures. In addition, a target-dependent kinematic model is invoked to characterize the target dynamics. Both generative models are integrated in a graphical framework for joint estimation of the target's kinematics, pose, and discrete valued identity. Finally, for target recognition we advocate the concept of a continuous identity manifold that captures both inter-class and intra-class shape variability among training targets. A hemispherical view manifold is used for modeling the view-dependent appearance. In addition to being able to deal with arbitrary view variations, this model can determine the target identity at both class and sub-class levels, for targets not present in the training data. The proposed components of the ATR system enable us to perform low computational complexity target detection with low false alarm rates, robust tracking of targets under challenging circumstances and recognition of target identities at both class and sub-class levels. Experiments on real and simulated data confirm the performance of the proposed components with promising results

Mining compositional features for boosting

The selection of weak classifiers is critical to the success of boosting techniques. Poor weak classifiers do not perform better than random guess, thus cannot help decrease the training error during the boosting process. Therefore, when constructing the weak classifier pool, we prefer the quality rather than the quantity of the weak classifiers. In this paper, we present a data mining-driven approach to discovering compositional features from a given and possibly small feature pool. Compared with individual features (e.g. weak decision stumps) which are of limited discriminative ability, the mined compositional features have guaranteed power in terms of the descriptive and discriminative abilities, as well as bounded training error. To cope with the combinatorial cost of discovering compositional features, we apply data mining methods (frequent itemset mining) to efficiently find qualified compositional features of any possible order. These weak classifiers are further combined through a multi-class AdaBoost method for final multi-class classification. Experiments on a challenging 10-class event recognition problem show that boosting compositional features can lead to faster decrease of training error and significantly higher accuracy compared to conventional boosting decision stumps. 1