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  • 1
    Language: English
    In: Pattern Recognition Letters, 15 December 2015, Vol.68, pp.1-8
    Description: Discriminative part-based models have become the approach for visual object detection. The models learn from a large number of positive and negative examples with annotated class labels and location (bounding box). In contrast, we propose a part-based generative model that learns from a small number of positive examples. This is achieved by utilizing “privileged information”, sparse class-specific landmarks with semantic meaning. Our method uses bio-inspired complex-valued Gabor features to describe local parts. Gabor features are transformed to part probabilities by unsupervised Gaussian Mixture Model (GMM). GMM estimation is robustified for a small amount of data by a randomization procedure inspired by random forests. The GMM framework is also used to construct a probabilistic spatial model of part configurations. Our detector is invariant to translation, rotation and scaling. On part level invariance is achieved by pose quantization which is more efficient than previously proposed feature transformations. In the spatial model, invariance is achieved by mapping parts to an “aligned object space”. Using a small number of positive examples our generative method performs comparably to the state-of-the-art discriminative method.
    Keywords: Gabor Feature ; Gaussian Mixture Model ; Object Detection ; Visual Classification ; Generative Learning ; Engineering ; Computer Science
    ISSN: 0167-8655
    E-ISSN: 1872-7344
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  • 2
    Language: English
    In: Image and Vision Computing, October 2018, Vol.78, pp.73-83
    Description: Managing the water quality of freshwaters is a crucial task worldwide. One of the most used methods to biomonitor water quality is to sample benthic macroinvertebrate communities, in particular to examine the presence and proportion of certain species. This paper presents a benchmark database for automatic visual classification methods to evaluate their ability for distinguishing visually similar categories of aquatic macroinvertebrate taxa. We make publicly available a new database, containing 64 types of freshwater macroinvertebrates, ranging in number of images per category from 7 to 577. The database is divided into three datasets, varying in number of categories (64, 29, and 9 categories). Furthermore, in order to accomplish a baseline evaluation performance, we present the classification results of Convolutional Neural Networks (CNNs) that are widely used for deep learning tasks in large databases. Besides CNNs, we experimented with several other well-known classification methods using deep features extracted from the data.
    Keywords: Biomonitoring ; Fine-Grained Classification ; Benthic Macroinvertebrates ; Deep Learning ; Convolutional Neural Networks ; Engineering ; Applied Sciences
    ISSN: 0262-8856
    E-ISSN: 1872-8138
    Source: ScienceDirect Journals (Elsevier)
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  • 3
    Language: English
    In: 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA), December 2016, pp.1-6
    Description: The k-nearest-neighbour classifiers (k-NN) have been one of the simplest yet most effective approaches to instance based learning problem for image classification. However, with the growth of the size of image datasets and the number of dimensions of image descriptors, popularity of k-NNs has decreased due to their significant storage requirements and computational costs. In this paper we propose a vector quantization (VQ) based k-NN classifier, which has improved efficiency for both storage requirements and computational costs. We test the proposed method on publicly available large scale image datasets and show that the proposed method performs comparable to traditional k-NN with significantly better complexity and storage requirements.
    Keywords: Vector Quantization ; Training ; Image Classification ; Computational Efficiency ; Classification Algorithms ; Feature Extraction ; K-NN Classifier ; Vector Quantization ; Large-Scale Image Classification ; Applied Sciences
    E-ISSN: 2154-512X
    Source: IEEE Conference Publications
    Source: IEEE Xplore
    Source: IEEE Journals & Magazines 
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  • 4
    Language: English
    In: 2016 23rd International Conference on Pattern Recognition (ICPR), December 2016, pp.2276-2281
    Description: Aquatic macroinvertebrate biomonitoring is an efficient way of assessment of slow and subtle anthropogenic changes and their effect on water quality. It is imperative to have reliable identification and counts of the various taxa occurring in samples as these form the basis for the quality indices used to infer the ecological status of the aquatic ecosystem. In this paper, we try to close the gap between human taxa identification accuracy (typically 90-95% on 30-40 classes of macroinvertebrates) and results of automatic fine-grained classification by introducing a novel technique based on Convolutional Neural Networks (CNN). CNN learns optimal features for macroinvertebrate classification and achieves near human accuracy when tested on 29 macroinvertebrate classes. Moreover, we perform comparative evaluation of the learned features against the hand-crafted features, which have been commonly used in classical approaches, and confirm superiority of the learned deep features over the engineered ones.
    Keywords: Feature Extraction ; Ecosystems ; Water Resources ; Machine Vision ; Microscopy ; Databases
    ISSN: 10514651
    Source: IEEE Conference Publications
    Source: IEEE Xplore
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  • 5
    Language: English
    In: 2016 ICPR 2nd Workshop on Computer Vision for Analysis of Underwater Imagery (CVAUI), December 2016, pp.43-48
    Description: The types and numbers of benthic macroinvertebrates found in a water body reflect water quality. Therefore, macroinvertebrates are routinely monitored as a part of freshwater ecological quality assessment. The collected macroinvertebrate samples are identified by human experts, which is costly and time-consuming. Thus, developing automated identification methods that could partially replace the human effort is important. In our group, we have been working toward this goal and, in this paper, we improve our earlier results on automated macroinvertebrate classification obtained using deep Convolutional Neural Networks (CNNs). We apply simple data enrichment prior to CNN training. By rotations and mirroring, we create new images so as to increase the total size of the image database sixfold. We evaluate the effect of data enrichment on Caffe and MatConvNet CNN implementations. The networks are trained either fully on the macroinvertebrate data or first pretrained using ImageNet pictures and then fine-tuned using the macroinvertebrate data. The results show 3-6% improvement, when the enriched data are used. This is an encouraging result, because it significantly narrows the gap between automated techniques and human experts, while it leaves room for future improvements as even the size of the enriched data, about 60000 images, is small compared to data sizes typically required for efficient training of deep CNNs.
    Keywords: Training ; Monitoring ; Feature Extraction ; Databases ; Testing ; Quality Assessment ; Convolution
    Source: IEEE Conference Publications
    Source: IEEE Xplore
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