Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

A survey of content-based image retrieval with high-level semantics

This survey aims at providing multimedia researchers with a state-of-the-art overview of fusion strategies, which are used for combining multiple modalities in order to accomplish various multimedia analysis tasks. The existing literature on multimodal fusion research is presented through several classifications based on the fusion methodology and the level of fusion (feature, decision, and hybrid). The fusion methods are described from the perspective of the basic concept, advantages, weaknesses, and their usage in various analysis tasks as reported in the literature. Moreover, several distinctive issues that influence a multimodal fusion process such as, the use of correlation and independence, confidence level, contextual information, synchronization between different modalities, and the optimal modality selection are also highlighted. Finally, we present the open issues for further research in the area of multimodal fusion.

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Multimodal fusion for multimedia analysis: a survey

This paper presents an approach to recognising the gender and expression of face images by means of active appearance models (AAM). Features extracted by a trained AAM are used to construct support vector machine (SVM) classifiers for 4 elementary emotional states (happy, angry, sad, neutral). These classifiers are arranged into a cascade structure in order to optimise overall recognition performance. Furthermore, it is shown how performance can be further improved by first classifying the gender of the face images using an SVM trained in a similar manner. Both gender-specific expression classification and expression-specific gender classification cascades are considered, with the former yielding better recognition performance. We conclude that there are gender-specific differences in the appearance of facial expressions that can be exploited for automated recognition, and that cascades are an efficient and effective way of performing multi-class recognition of facial expressions.

/pdf/cascaded-classification-of-gender-and-facial-expression-3x3dd7miml.pdf

Cascaded classification of gender and facial expression using active appearance models

It is unclear how birds differentiate their own eggs from cuckoo’s eggs that parasitize their nests. Here, the authors develop a computer vision tool that simulates how brains process pattern information and show that cuckoos’ hosts have evolved unique egg patterns to distinguish their own eggs from a cuckoo’s.

/pdf/pattern-recognition-algorithm-reveals-how-birds-evolve-1cw2gypcgk.pdf

Pattern recognition algorithm reveals how birds evolve individual egg pattern signatures

This paper presents an approach to designing and implementing extensible computational models for perceiving systems based on a knowledge-driven joint inference approach. These models can integrate different sources of information both horizontally (multi-modal and temporal fusion) and vertically (bottom–up, top–down) by incorporating prior hierarchical knowledge expressed as an extensible ontology.Two implementations of this approach are presented. The first consists of a content-based image retrieval system that allows users to search image databases using an ontological query language. Queries are parsed using a probabilistic grammar and Bayesian networks to map high-level concepts onto low-level image descriptors, thereby bridging the ‘semantic gap’ between users and the retrieval system. The second application extends the notion of ontological languages to video event detection. It is shown how effective high-level state and event recognition mechanisms can be learned from a set of annotated training sequences by incorporating syntactic and semantic constraints represented by an ontology.

Ontological inference for image and video analysis

For species which bear unique markings, such as natural spot patterning, field work has become increasingly more reliant on visual identification to recognize and catalog particular specimens or to monitor individuals within populations. While many species of interest exhibit characteristic markings that in principle allow individuals to be identified from photographs, scientists are often faced with the task of matching observations against databases of hundreds or thousands of images. We present a novel technique for automated identification of manta rays (Manta alfredi and Manta birostris) by means of a pattern-matching algorithm applied to images of their ventral surface area. Automated visual identification has recently been developed for several species. However, such methods are typically limited to animals that can be photographed above water, or whose markings exhibit high contrast and appear in regular constellations. While manta rays bear natural patterning across their ventral surface, these patterns vary greatly in their size, shape, contrast, and spatial distribution. Our method is the first to have proven successful at achieving high matching accuracies on a large corpus of manta ray images taken under challenging underwater conditions. Our method is based on automated extraction and matching of keypoint features using the Scale-Invariant Feature Transform (SIFT) algorithm. In order to cope with the considerable variation in quality of underwater photographs, we also incorporate preprocessing and image enhancement steps. Furthermore, we use a novel pattern-matching approach that results in better accuracy than the standard SIFT approach and other alternative methods. We present quantitative evaluation results on a data set of 720 images of manta rays taken under widely different conditions. We describe a novel automated pattern representation and matching method that can be used to identify individual manta rays from photographs. The method has been incorporated into a website (mantamatcher.org) which will serve as a global resource for ecological and conservation research. It will allow researchers to manage and track sightings data to establish important life-history parameters as well as determine other ecological data such as abundance, range, movement patterns, and structure of manta ray populations across the world.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.587

Manta Matcher: automated photographic identification of manta rays using keypoint features

https://www.cl.cam.ac.uk/~cpt23/papers/TownIVC2004.pdf

Christopher Town

Papers

Cascaded classification of gender and facial expression using active appearance models

Pattern recognition algorithm reveals how birds evolve individual egg pattern signatures

Ontological inference for image and video analysis

Manta Matcher: automated photographic identification of manta rays using keypoint features

Language-based querying of image collections on the basis of an extensible ontology