Dimensionality reduction by minimizing nearest-neighbor classification error

There is a great interest in dimensionality reduction techniques for tackling the problem of high-dimensional pattern classification. This paper addresses the topic of supervised learning of a linear dimension reduction mapping suitable for classification problems. The proposed optimization procedure is based on minimizing an estimation of the nearest neighbor classifier error probability, and it learns a linear projection and a small set of prototypes that support the class boundaries. The learned classifier has the property of being very computationally efficient, making the classification much faster than state-of-the-art classifiers, such as SVMs, while having competitive recognition accuracy. The approach has been assessed through a series of experiments, showing a uniformly good behavior, and competitive compared with some recently proposed supervised dimensionality reduction techniques. © 2010 Elsevier B.V. All rights reserved.Work partially supported by the Spanish projects TIN2008-04571 and Consolider Ingenio 2010: MIPRCV (CSD2007-00018).Villegas, M.; Paredes Palacios, R. (2011). Dimensionality reduction by minimizing nearest-neighbor classification error. Pattern Recognition Letters. 32(4):633-639. https://doi.org/10.1016/j.patrec.2010.12.002S63363932

Contributions to High-Dimensional Pattern Recognition

This thesis gathers some contributions to statistical pattern recognition particularly targeted at problems in which the feature vectors are high-dimensional. Three pattern recognition scenarios are addressed, namely pattern classification, regression analysis and score fusion. For each of these, an algorithm for learning a statistical model is presented. In order to address the difficulty that is encountered when the feature vectors are high-dimensional, adequate models and objective functions are defined. The strategy of learning simultaneously a dimensionality reduction function and the pattern recognition model parameters is shown to be quite effective, making it possible to learn the model without discarding any discriminative information. Another topic that is addressed in the thesis is the use of tangent vectors as a way to take better advantage of the available training data. Using this idea, two popular discriminative dimensionality reduction techniques are shown to be effectively improved. For each of the algorithms proposed throughout the thesis, several data sets are used to illustrate the properties and the performance of the approaches. The empirical results show that the proposed techniques perform considerably well, and furthermore the models learned tend to be very computationally efficient.Villegas Santamaría, M. (2011). Contributions to High-Dimensional Pattern Recognition [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/10939Palanci

다양한 딥 러닝 학습 환경 하의 컨텐츠 기반 이미지 검색

학위논문(박사) -- 서울대학교대학원 : 공과대학 전기·정보공학부, 2022.2. 조남익.방대한 데이터베이스에서 질의에 대한 관련 이미지를 찾는 콘텐츠 기반 이미지 검색은 컴퓨터 비전 분야의 근본적인 작업 중 하나이다. 특히 빠르고 정확한 검색을 수행하기 위해 해싱 (Hashing) 및 곱 양자화 (Product Quantization, PQ) 로 대표되는 근사최근접 이웃 (Approximate Nearest Neighbor, ANN) 검색 방식이 이미지 검색 커뮤니티에서 주목받고 있다. 신경망 기반 딥 러닝 (CNN-based deep learning) 이 많은 컴퓨터 비전 작업에서 우수한 성능을 보여준 이후로, 해싱 및 곱 양자화 기반 이미지 검색 시스템 모두 개선을 위해 딥 러닝을 채택하고 있다. 본 학위 논문에서는 적절한 검색 시스템을 제안하기 위해 다양한 딥 러닝 학습 환경아래에서 이미지 검색 방법을 제안한다. 구체적으로, 이미지 검색의 목적을 고려하여 의미적으로 유사한 이미지를 검색하는 딥 러닝 해싱 시스템을 개발하기 위한 지도 학습 방법을 제안하고, 의미적, 시각적으로 모두 유사한 이미지를 검색하는 딥 러닝 곱 양자화 기반의 시스템을 구축하기 위한 준지도, 비지도 학습 방법을 제안한다. 또한, 이미지 검색 데이터베이스의 특성을 고려하여, 분류해야할 클래스 (class category) 가 많은 얼굴 이미지 데이터 세트와 하나 이상의 레이블 (label) 이 지정된 일반 이미지 세트를 분리하여 따로 검색 시스템을 구축한다. 먼저 이미지에 부여된 의미론적 레이블을 사용하는 지도 학습을 도입하여 해싱 기반 검색 시스템을 구축한다. 클래스 간 유사성 (다른 사람 사이의 유사한 외모) 과 클래스 내 변화(같은 사람의 다른 포즈, 표정, 조명) 와 같은 얼굴 이미지 구별의 어려움을 해결하기 위해 각 이미지의 클래스 레이블을 사용한다. 얼굴 이미지 검색 품질을 더욱 향상시키기 위해 SGH (Similarity Guided Hashing) 방식을 제안하며, 여기서 다중 데이터 증강 결과를 사용한 자기 유사성 학습이 훈련 중에 사용된다. 그리고 해싱 기반의 일반 이미지 검색 시스템을 구성하기 위해 DHD(Deep Hash Distillation) 방식을 제안한다. DHD에서는 지도 신호를 활용하기 위해 클래스별 대표성을 나타내는 훈련 가능한 해시 프록시 (proxy) 를 도입한다. 또한, 해싱에 적합한 자체 증류 기법을 제안하여 증강 데이터의 잠재력을 일반적인 이미지 검색 성능 향상에 적용한다. 둘째로, 레이블이 지정된 이미지 데이터와 레이블이 지정되지 않은 이미지 데이터를 모두 활용하는 준지도 학습을 조사하여 곱 양자화 기반 검색 시스템을 구축한다. 지도 학습 딥 러닝 기반의 이미지 검색 방법들은 우수한 성능을 보이려면 값비싼 레이블 정보가 충분해야 한다는 단점이 있다. 게다가, 레이블이 지정되지 않은 수많은 이미지 데이터는 훈련에서 제외된다는 한계가 있다. 이 문제를 해결하기 위해 벡터 양자화 기반 반지도 영상 검색 방식인 GPQ (Generalized Product Quantization) 네트워크를 제안한다. 레이블이 지정된 데이터 간의 의미론적 유사성을 유지하는 새로운 메트릭 학습 (Metric learning) 전략과 레이블이 지정되지 않은 데이터의 고유한 잠재력을 최대한 활용하는 엔트로피 정규화 방법을 사용하여 검색 시스템을 개선한다. 이 솔루션은 양자화 네트워크의 일반화 용량을 증가시켜 이전의 한계를 극복할 수 있게한다. 마지막으로, 딥 러닝 모델이 사람의 지도 없이 시각적으로 유사한 이미지 검색을 수행할 수 있도록 하기 위해 비지도 학습 알고리즘을 탐색한다. 비록 레이블 주석을 활용한 심층 지도 기반의 방법들이 기존 방법들에 대비 우수한 검색 성능을 보일지라도, 방대한 양의 훈련 데이터에 대해 정확하게 레이블을 지정하는 것은 힘들고 주석에서 오류가 발생하기 쉽다는 한계가 있다. 이 문제를 해결하기 위해 레이블 없이 자체 지도 방식으로 훈련하는 SPQ (Self-supervised Product Quantization) 네트워크 라는 심층 비지도 이미지 검색 방법을 제안한다. 새롭게 설계된 교차 양자화 대조 학습 방식으로 서로 다르게 변환된 이미지를 비교하여 곱 양자화의 코드워드와 심층 시각적 표현을 동시에 학습한다. 이 방식을 통해 이미지에 내제된 내용을 별도의 사람 지도 없이 네트워크가 스스로 이해하게 되고, 시각적으로 정확한 검색을 수행할 수 있는 설명 기능을 추출할 수 있게 된다. 벤치마크 데이터 세트에 대한 광범위한 이미지 검색 실험을 수행하여 제안된 방법이 다양한 평가 프로토콜에서 뛰어난 결과를 산출함을 확인했다. 지도 학습 기반의 얼굴 영상 검색의 경우 SGH는 저해상도 및 고해상도 얼굴 영상 모두에서 최고의 검색 성능을 달성하였고, DHD는 최고의 검색 정확도로 일반 영상 검색 실험에서 효율성을 입증한다. 준지도 일반 이미지 검색의 경우 GPQ는 레이블이 있는 이미지 데이터와 레이블이 없는 이미지 데이터를 모두 사용하는 프로토콜에 대한 최상의 검색 결과를 보여준다. 마지막으로, 비지도 학습 이미지 검색의 경우 지도 방식으로 미리 학습된 초기 값 없이도 SPQ를 사용하여 최상의 검색 점수를 얻었으며 시각적으로 유사한 이미지가 검색 결과로 성공적으로 검색되는 것을 관찰할 수 있다.Content-based image retrieval, which finds relevant images to a query from a huge database, is one of the fundamental tasks in the field of computer vision. Especially for conducting fast and accurate retrieval, Approximate Nearest Neighbor (ANN) search approaches represented by Hashing and Product Quantization (PQ) have been proposed to image retrieval community. Ever since neural network based deep learning has shown excellent performance in many computer vision tasks, both Hashing and product quantization-based image retrieval systems are also adopting deep learning for improvement. In this dissertation, image retrieval methods under various deep learning conditions are investigated to suggest the appropriate retrieval systems. Specifically, by considering the purpose of image retrieval, the supervised learning methods are proposed to develop the deep Hashing systems that retrieve semantically similar images, and the semi-supervised, unsupervised learning methods are proposed to establish the deep product quantization systems that retrieve both semantically and visually similar images. Moreover, by considering the characteristics of image retrieval database, the face image sets with numerous class categories, and the general image sets of one or more labeled images are separated to be explored when building a retrieval system. First, supervised learning with the semantic labels given to images is introduced to build a Hashing-based retrieval system. To address the difficulties of distinguishing face images, such as the inter-class similarities (similar appearance between different persons) and the intra-class variations (same person with different pose, facial expressions, illuminations), the identity label of each image is employed to derive the discriminative binary codes. To further develop the face image retrieval quality, Similarity Guided Hashing (SGH) scheme is proposed, where the self-similarity learning with multiple data augmentation results are employed during training. In terms of Hashing-based general image retrieval systems, Deep Hash Distillation (DHD) scheme is proposed, where the trainable hash proxy that presents class-wise representative is introduced to take advantage of supervised signals. Moreover, self-distillation scheme adapted for Hashing is utilized to improve general image retrieval performance by exploiting the potential of augmented data appropriately. Second, semi-supervised learning that utilizes both labeled and unlabeled image data is investigated to build a PQ-based retrieval system. Even if the supervised deep methods show excellent performance, they do not meet the expectations unless expensive label information is sufficient. Besides, there is a limitation that a tons of unlabeled image data is excluded from training. To resolve this issue, the vector quantization-based semi-supervised image retrieval scheme: Generalized Product Quantization (GPQ) network is proposed. A novel metric learning strategy that preserves semantic similarity between labeled data, and a entropy regularization term that fully exploits inherent potentials of unlabeled data are employed to improve the retrieval system. This solution increases the generalization capacity of the quantization network, which allows to overcome previous limitations. Lastly, to enable the network to perform a visually similar image retrieval on its own without any human supervision, unsupervised learning algorithm is explored. Although, deep supervised Hashing and PQ methods achieve the outstanding retrieval performances compared to the conventional methods by fully exploiting the label annotations, however, it is painstaking to assign labels precisely for a vast amount of training data, and also, the annotation process is error-prone. To tackle these issues, the deep unsupervised image retrieval method dubbed Self-supervised Product Quantization (SPQ) network, which is label-free and trained in a self-supervised manner is proposed. A newly designed Cross Quantized Contrastive learning strategy is applied to jointly learn the PQ codewords and the deep visual representations by comparing individually transformed images (views). This allows to understand the image content and extract descriptive features so that the visually accurate retrieval can be performed. By conducting extensive image retrieval experiments on the benchmark datasets, the proposed methods are confirmed to yield the outstanding results under various evaluation protocols. For supervised face image retrieval, SGH achieves the best retrieval performance for both low and high resolution face image, and DHD also demonstrates its efficiency in general image retrieval experiments with the state-of-the-art retrieval performance. For semi-supervised general image retrieval, GPQ shows the best search results for protocols that use both labeled and unlabeled image data. Finally, for unsupervised general image retrieval, the best retrieval scores are achieved with SPQ even without supervised pre-training, and it can be observed that visually similar images are successfully retrieved as search results.Abstract i Contents iv List of Tables vii List of Figures viii 1 Introduction 1 1.1 Contribution 3 1.2 Contents 4 2 Supervised Learning for Deep Hashing: Similarity Guided Hashing for Face Image Retrieval / Deep Hash Distillation for General Image Retrieval 5 2.1 Motivation and Overview for Face Image Retrieval 5 2.1.1 Related Works 9 2.2 Similarity Guided Hashing 10 2.3 Experiments 16 2.3.1 Datasets and Setup 16 2.3.2 Results on Small Face Images 18 2.3.3 Results on Large Face Images 19 2.4 Motivation and Overview for General Image Retrieval 20 2.5 Related Works 22 2.6 Deep Hash Distillation 24 2.6.1 Self-distilled Hashing 24 2.6.2 Teacher loss 27 2.6.3 Training 29 2.6.4 Hamming Distance Analysis 29 2.7 Experiments 32 2.7.1 Setup 32 2.7.2 Implementation Details 32 2.7.3 Results 34 2.7.4 Analysis 37 3 Semi-supervised Learning for Product Quantization: Generalized Product Quantization Network for Semi-supervised Image Retrieval 42 3.1 Motivation and Overview 42 3.1.1 Related Work 45 3.2 Generalized Product Quantization 47 3.2.1 Semi-Supervised Learning 48 3.2.2 Retrieval 52 3.3 Experiments 53 3.3.1 Setup 53 3.3.2 Results and Analysis 55 4 Unsupervised Learning for Product Quantization: Self-supervised Product Quantization for Deep Unsupervised Image Retrieval 58 4.1 Motivation and Overview 58 4.1.1 Related Works 61 4.2 Self-supervised Product Quantization 62 4.2.1 Overall Framework 62 4.2.2 Self-supervised Training 64 4.3 Experiments 67 4.3.1 Datasets 67 4.3.2 Experimental Settings 68 4.3.3 Results 71 4.3.4 Empirical Analysis 71 5 Conclusion 75 Abstract (In Korean) 88박

Subspace Methods for Face Recognition: Singularity, Regularization, and Robustness

Face recognition has been an important issue in computer vision and pattern recognition over the last several decades (Zhao et al., 2003). While human can recognize faces easily, automated face recognition remains a great challenge in computer-based automated recognition research. One difficulty in face recognition is how to handle the variations in expression, pose an

Dissimilarity-based Ensembles for Multiple Instance Learning

In multiple instance learning, objects are sets (bags) of feature vectors (instances) rather than individual feature vectors. In this paper we address the problem of how these bags can best be represented. Two standard approaches are to use (dis)similarities between bags and prototype bags, or between bags and prototype instances. The first approach results in a relatively low-dimensional representation determined by the number of training bags, while the second approach results in a relatively high-dimensional representation, determined by the total number of instances in the training set. In this paper a third, intermediate approach is proposed, which links the two approaches and combines their strengths. Our classifier is inspired by a random subspace ensemble, and considers subspaces of the dissimilarity space, defined by subsets of instances, as prototypes. We provide guidelines for using such an ensemble, and show state-of-the-art performances on a range of multiple instance learning problems.Comment: Submitted to IEEE Transactions on Neural Networks and Learning Systems, Special Issue on Learning in Non-(geo)metric Space

NNMap: A method to construct a good embedding for nearest neighbor classification

a b s t r a c t This paper aims to deal with the practical shortages of nearest neighbor classifier. We define a quantitative criterion of embedding quality assessment for nearest neighbor classification, and present a method called NNMap to construct a good embedding. Furthermore, an efficient distance is obtained in the embedded vector space, which could speed up nearest neighbor classification. The quantitative quality criterion is proposed as a local structure descriptor of sample data distribution. Embedding quality corresponds to the quality of the local structure. In the framework of NNMap, one-dimension embeddings act as weak classifiers with pseudo-losses defined on the amount of the local structure preserved by the embedding. Based on this property, the NNMap method reduces the problem of embedding construction to the classical boosting problem. An important property of NNMap is that the embedding optimization criterion is appropriate for both vector and non-vector data, and equally valid in both metric and non-metric spaces. The effectiveness of the new method is demonstrated by experiments conducted on the MNIST handwritten dataset, the CMU PIE face images dataset and the datasets from UCI machine learning repository