Romain Raveaux

Structured representations in a content based image retrieval context

Here, we propose an automatic system to annotate and retrieve images. We assume that regions in an image can be described using a vocabulary of blobs. Blobs are generated from image features using clustering. Features are locally extracted on regions to capture Color, Texture and Shape information. Regions are processed by an efficient segmentation algorithm. Images are structured into a region adjacency graph to consider spatial relationships between regions. This representation is used to perform a similarity search into an image set. Hence, the user can express his need by giving a query image, and thereafter receiving as a result all similar images. Our graph based approach is benchmarked to conventional Bag of Words methods. Results tend to reveal a good behavior in classification of our graph based solution on two publicly available databases. Experiments illustrate that a structural approach requires a smaller vocabulary size to reach its best performance.

Learning graph prototypes for shape recognition

This paper presents some new approaches for computing graph prototypes in the context of the design of a structural nearest prototype classifier. Four kinds of prototypes are investigated and compared: set median graphs, generalized median graphs, set discriminative graphs and generalized discriminative graphs. They differ according to (i) the graph space where they are searched for and (ii) the objective function which is used for their computation. The first criterion allows to distinguish set prototypes which are selected in the initial graph training set from generalized prototypes which are generated in an infinite set of graphs. The second criterion allows to distinguish median graphs which minimize the sum of distances to all input graphs of a given class from discriminative graphs, which are computed using classification performance as criterion, taking into account the inter-class distribution. For each kind of prototype, the proposed approach allows to identify one or many prototypes per class, in order to manage the trade-off between the classification accuracy and the classification time. Each graph prototype generation/selection is performed through a genetic algorithm which can be specialized to each case by setting the appropriate encoding scheme, fitness and genetic operators. An experimental study performed on several graph databases shows the superiority of the generation approach over the selection one. On the other hand, discriminative prototypes outperform the generative ones. Moreover, we show that the classification rates are improved while the number of prototypes increases. Finally, we show that discriminative prototypes give better results than the median graph based classifier.

A Graph Database Repository and Performance Evaluation Metrics for Graph Edit Distance

Graph edit distance (GED) is an error tolerant graph matching paradigm whose methods are often evaluated in a classification context and less deeply assessed in terms of the accuracy of the found solution. To evaluate the accuracy of GED methods, low level information is required not only at the classification level but also at the matching level. Most of the publicly available repositories with associated ground truths are dedicated to evaluating graph classification or exact graph matching methods and so the matching correspondences as well as the distance between each pair of graphs are not directly evaluated. This paper consists of two parts. First, we provide a graph database repository annotated with low level information like graph edit distances and their matching correspondences. Second, we propose a set of performance evaluation metrics to assess the performance of GED methods.

An Exact Graph Edit Distance Algorithm for Solving Pattern Recognition Problems

Graph edit distance is an error tolerant matching technique emerged as a powerful and flexible graph matching paradigm that can be used to address different tasks in pattern recognition, machine learning and data mining; it represents the minimum-cost sequence of basic edit operations to transform one graph into another by means of insertion, deletion and substitution of vertices and/or edges. A widely used method for exact graph edit distance computation is based on the A* algorithm. To overcome its high memory load while traversing the search tree for storing pending solutions to be explored, we propose a depth-first graph edit distance algorithm which requires less memory and searching time. An evaluation of all possible solutions is performed without explicitly enumerating them all. Candidates are discarded using an upper and lower bounds strategy. A solid experimental study is proposed; experiments on a publicly available database empirically demonstrated that our approach is better than the A* graph edit distance computation in terms of speed, accuracy and classification rate.

A survey on image-based insect classification

Entomology 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. Graphical abstractDisplay Omitted HighlightsFourty-four about image-based insect recognition are scrutinized.Each paper is qualified from three perspectives: image capture, feature extraction and classification.Datasets used in the literature are investigated.A discussion is given in which several questions about the problem are raised.

A graph classification approach using a multi-objective genetic algorithm application to symbol recognition

In this paper, a graph classification approach based on a multi-objective genetic algorithm is presented. The method consists in the learning of sets composed of synthetic graph prototypes which are used for a classification step. These learning graphs are generated by simultaneously maximizing the recognition rate while minimizing the confusion rate. Using such an approach the algorithm provides a range of solutions, the couples (confusion, recognition) which suit to the needs of the system. Experiments are performed on real data sets, representing 10 symbols. These tests demonstrate the interest to produce prototypes instead of finding representatives which simply belong to the data set.

A colour text/graphics separation based on a graph representation

In this paper, a colour text/graphics segmentation is proposed. Firstly, it takes advantage of colour properties by computing a relevant hybrid colour model. Then an edge detection is performed to construct a binary image composed of contour information. From this contour image, connected components are classified according to a graph representation. Text and graphic diversity is taken into account by a prototype selection scheme for structural data. Finally, the approach is evaluated on colour cadastral maps and a comparative study is presented.

New binary linear programming formulation to compute the graph edit distance

Abstract In this paper, a new binary linear programming formulation for computing the exact Graph Edit Distance (GED) between two graphs is proposed. A fundamental strength of the formulations lies in their genericity since the GED can be computed between directed or undirected fully attributed graphs. Moreover, a continuous relaxation of the domain constraints in the formulation provides an efficient lower bound approximation of the GED. A complete experimental study that compares the proposed formulations with six state-of-the-art algorithms is provided. By considering both the accuracy of the proposed solution and the efficiency of the algorithms as performance criteria, the results show that none of the compared methods dominate the others in the Pareto sense. In general, our formulation converges faster to optimality while being able to scale up to match the largest graphs in our experiments. The relaxed formulation leads to an accurate approach that is 12% more accurate than the best approximate method of our benchmark.

Object Extraction from Colour Cadastral Maps

In this paper, an object extraction method from ancient colour maps is proposed. It consists on the localization of quarters inside a given cadastral map. The colour aspect is exploited thanks to a colour restoration algorithm and the selection of a relevant hybrid colour model. Objects composing the map are located using a multi-components gradient. To identify quarters, a peeling the onion method is adopted. This selective method starts by separated text and graphics. On the graphic layer, a connected component analysis is carried out through the use of a neighbourhood graph. This graph is smartly pruned to consider only significant areas. Consequently, the quarter boundaries are found using a snake which is a computer-generated curve that moves within an image to fit a given object. The performance of our method is measured up in two steps: Firstly, the colour space selection is assessed according to the colour distinction capacity while being robust to variations/noise then the automatic extraction approach is compared to the user ground truth. Results show the good behaviour of the whole system.

A Colour Document Interpretation: Application to Ancient Cadastral Maps

In this paper, a colour graphic document analysis is proposed with an application to ancient cadastral maps. The approach relies on the idea that images of document are fairly different than usual images, such as natural scenes or paintings. From this statement, we present an architecture for colour document understanding. It is based on two paradigms. Firstly, a dedicated colour representation named adapted colour space which aims to learn the image colour specificity and secondly a document oriented segmentation using a region growing algorithm supervised by a hierarchical strategy. Experiments are performed to judge the whole process and the first results show a good behaviour in term of information retrieval.