International audienceEntomology has had many applications in many biological domains (i.e insect counting as a biodiversity index). To meet a growing biological demand and to compensate a decreasing workforce amount, automated entomology has been around for decades. This challenge has been tackled by computer scientists as well as by biologists themselves. This survey investigates fourty-four studies on this topic and tries to give a global picture on what are the scientific locks and how the problem was addressed. Views are adopted on image capture, feature extraction, classification methods and the tested datasets. A general discussion is finally given on the questions that might still remain unsolved such as: the image capture conditions mandatory to good recognition performance, the definition of the problem and whether computer scientist should consider it as a problem in its own or just as an instance of a wider image recognition problem

Many tasks in computer vision and pattern recognition are formulated as graph matching problems. Despite the NP-hard nature of the problem, fast and accurate approximations have led to significant progress in a wide range of applications. Learning graph matching functions from observed data, however, still remains a challenging issue. This paper presents an effective scheme to parametrize a graph model for object matching in a classification context. For this, we propose a representation based on a parametrized model graph, and optimize it to increase a classification rate. Experimental evaluations on real datasets demonstrate the effectiveness (in terms of accuracy and speed) of our approach against graph classification with hand-crafted cost functions.

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The quality of document images has direct impacts on the performance of document image processing systems. Document Image Quality Assessment (DIQA) is, therefore, of fundamental importance to a numerous document processing applications. As manual quality assessment is almost impossible for a huge volume of document images generated in day-today life, it is critical to develop intelligent machine operated methods to estimate the quality of document images. In this paper, a blind document image quality assessment method is proposed to deal with the problem of DIQA in real scenarios, as reference images are not always available. To estimate the quality of a document image, the document is rst sampled into a set of patches. The extracted patches are then ltered out based on their level of foreground information using a patch selection strategy. For every selected patch, a cluster assignment is then performed to obtain its quality from a quality aware bag of visual words constructed using k-means clustering. An average pooling is nally employed to estimate the quality of the input document image. To evaluate the proposed method, a dataset composed of document images and three scene image datasets were considered for experimentation. The results obtained from the proposed method demonstrate the eectiveness of the proposed DIQA method. These achievements in applied computational intelligence, expert and decision support systems make a good foundation for creating practical tools to automate document image forgery detection, and archiving process.

Insects are living beings whose utility is critical in life sciences. They enable biologists obtaining knowledge on natural landscapes (for example on their health). Nevertheless, insect identification is timeconsuming and requires experienced workforce. To ease this task, we propose to turn it into an image-based pattern recognition problem by recognizing the insect from a photo. In this paper state-of-art deep convolutional architectures are used to tackle this problem. However, a limitation to the use of deep CNNs is the lack of data and the discrepancies in classes cardinality. To deal with such limitations, transfer learning is used to apply knowledge learnt from ImageNet-1000 recognition task to insect image recognition task. A question arises from transfer-learning : is it relevant to retrain the entire network or is it better not to modify some layers weights? The hypothesis behind this question is that there must be part of the network which contains generic (problem-independent) knowledge and the other one contains problem-specific knowledge. Tests have been conducted on two different insect image datasets. VGG-16 models were adapted to be more easily learnt. VGG-16 models were trained a) from scratch b) from ImageNet-1000. An advanced study was led on one of the datasets in which the influences on performance of two parameters were investigated: 1) The amount of learning data 2) The number of layers to be finetuned. It was determined VGG-16 last block is enough to be relearnt. We have made the code of our experiment as well as the script for generating an annotated insect dataset from ImageNet publicly available.

Convolutional neural networks (CNNs), in a few decades, have outperformed the existing state of the art methods in classication context. However, in the way they were formalised, CNNs are bound to operate on euclidean spaces. Indeed, convolution is a signal operation that are dened on euclidean spaces. This has restricted deep learning main use to euclidean-dened data such as sound or image. And yet, numerous computer application elds (among which network analysis, computational social science, chemo-informatics or computer graphics) induce non-euclideanly dened data such as graphs, networks or manifolds. In this paper we propose a new convolution neural network architecture, dened directly into graph space. The convolution operator is dened in graph domain thanks to a graph matching procedure between the input signal and a lter. We show its usability in a back-propagation context. Experimental results show that our model performance is at state of the art level on simple tasks. It shows robustness with respect to graph domain changes and improvement with respect to other euclidean and non-euclidean convolutional architectures.