In the last few years, there has been considerable interest in scene parsing. This task consists of assigning a predefined class label to each pixel (or pre-segmented region) in an image. To best address the complexity challenge of this task, first, we propose a new geometric retrieval strategy to select nearest neighbors from a database containing fully segmented and annotated images. Then, we introduce a novel and simple energy-minimization model. The proposed cost function of this model combines efficiently different global nonparametric semantic likelihood energy terms. These terms are computed from the (pre-)segmented regions of the (query) image and their structural properties (location, texture, color, context, and shape). Different from the traditional approaches, we use a simple and local optimization procedure derived from the iterative conditional modes algorithm to optimize our energy-based model. Experimental results on two challenging datasets: 1) microsoft research Cambridge dataset and 2) Stanford background dataset demonstrate the feasibility and the success of the proposed approach. Compared to existing annotation methods that require training classifiers for each object and learning many parameters, our method is easy to implement, has a few parameters, and combines different criteria.

Dynamic texture (DT) segmentation is the problem of clustering into groups various characteristics and phenomena that reproduce in both time and space, assigning a unique label to each group or region. Though this problem is highly complex, it has recently become the focus of considerable interest. This paper presents a simple and effective fusion framework for dynamic texture segmentation, whose objective is to combine multiple and weak region-based segmentation maps to get a final better segmentation result. The different label fields to be fused, are given by a simple clustering technique applied to an input video (based on three orthogonal planes xy, xt and yt). This is using as features a set of values of the requantized local binary patterns (LBP) histogram around the pixel to be classified. Promising preliminary experimental results have been achieved by our method on the challenging SynthDB dataset. Compared to existing dynamic texture segmentation approaches that require estimation of parameters or training classifiers, our method is easy to implement, simple and has few parameters.

Semantic image segmentation has recently become the focus of considerable interest. This task consists in assigning a predefined class label to each pixel (or pre-segmented region) in an image. To address the complexity challenge of this task, we develop, in this work, a novel and simple energy-minimization model. The proposed cost function of this model combines efficiently different global non-parametric semantic likelihood energy terms computed from the (pre-)segmented regions of the (query) image and their structural properties (location, texture, color, context and shape). To optimize our energy-based model, we use a local optimization procedure derived from the iterative conditional modes (ICM) algorithm. Experimental results on the challenging Microsoft Research Cambridge dataset (MSRC-21) clearly shows the feasibility and the merits of the proposed approach.

Image segmentation fusion is defined as the set of methods which aim at merging several image segmentations, in a manner that takes full advantage of the complementarity of each one. Previous relevant researches in this field have been impeded by the difficulty in identifying an appropriate single segmentation fusion criterion, providing the best possible, i.e., the more informative, result of fusion. In this paper, we propose a new model of image segmentation fusion based on multi-objective optimization which can mitigate this problem, to obtain a final improved result of segmentation. Our fusion framework incorporates the dominance concept in order to efficiently combine and optimize two complementary segmentation criteria, namely, the global consistency error and the F-measure (precision-recall) criterion. To this end, we present a hierarchical and efficient way to optimize the multi-objective consensus energy function related to this fusion model, which exploits a simple and deterministic iterative relaxation strategy combining the different image segments. This step is followed by a decision making task based on the so-called "technique for order performance by similarity to ideal solution". Results obtained on two publicly available databases with manual ground truth segmentations clearly show that our multi-objective energy-based model gives better results than the classical mono-objective one.

In this paper, we introduce a new fusion model whose objective is to fuse multiple region-based segmentation maps to get a final better segmentation result. The suggested new fusion model is based on an energy function originated from the global consistency error (GCE), a perceptual measure which takes into account the inherent multiscale nature of an image segmentation by measuring the level of refinement existing between two spatial partitions. Combined with a region merging/splitting prior, this new energy-based fusion model of label fields allows to define an interesting penalized likelihood estimation procedure based on the GCE criterion with which the fusion of basic, rapidly-computed segmentation results appears as a relevant alternative compared with other (possibly complex) segmentation techniques proposed in the image segmentation field. The performance of our fusion model was evaluated on the Berkeley dataset including various segmentations given by humans (manual ground truth segmentations). The obtained results clearly demonstrate the efficiency of this fusion model.

In this work, we introduce a new fusion model whose objective is to fuse multiple region-based segmentation maps to get a final better segmentation result. This new fusion model is based on an energy function originated from the global consistency error (GCE), a perceptual measure which takes into account the inherent multiscale nature of an image segmentation by measuring the level of refinement existing between two spatial partitions. Combined with a region merging/splitting prior, this new energy-based fusion model of label fields allows to define an interesting penalized likelihood estimation procedure based on the global consistency error criterion with which the fusion of basic, rapidly-computed segmentation results appears as a relevant alternative compared with other segmentation techniques proposed in the image segmentation field. The performance of our fusion model was evaluated on the Berkeley dataset including various segmentations given by humans.

Fusion of image segmentations using consensus clustering and based on the optimization of a single criterion (commonly called the median partition based approach) may bias and limit the performance of an image segmentation model. To address this issue, we propose, in this paper, a new fusion model of image segmentation based on multi-objective optimization which aims to avoid the bias caused by a single criterion and to achieve a final improved segmentation. The proposed fusion model combines two conflicting and complementary segmentation criteria, namely; the region-based variation of information (VoI) criterion and the contour-based F-Measure (precision-recall) criterion with an entropy-based confidence weighting factor. To optimize our energy-based model we use an optimization procedure derived from the iterative conditional modes (ICM) algorithm. The experimental results on the Berkeley database with manual ground truth segmentations clearly show the effectiveness and the robustness of our multi-objective median partition based approach.

Recently, there has been renewed interest in the fusion of image segmentation. However, previous relevant research has been impeded by the lack of an appropriate single segmentation criterion, which yields an improved final segmentation result. This paper proposes a new framework to tackle this problem. It is based on multi-objective optimization strategy, followed by a decision making technique called: technique for order performance by similarity to ideal solution (TOPSIS). This new fusion framework aims to overcome the limits caused by using a single criterion by combining and optimizing, simultaneously, two different and complementary segmentation criteria; namely, the global consistency error (GCE) (region-based criterion) and the F-measure (edge-based criterion). This new multi-criterion fusion framework is validated on the Berkeley image dataset and compared to different segmentation algorithms (with or without fusion strategy). Experiments show that the results of our new multi-objective approach improve the state of the art in terms of popular indices.

This paper presents a mathematical model that aims at describing and to resolving the Stochastic DRP with an approach based on the NSGAII. DRP consists in taking the passenger from a place of departure to a place of arrival. The ultimate aim is to offer an alternative to individually and collectively optimized displacements. The DRP is classified as NP-hard problem. That's why, most researches have been concentrated on the use of approximate methods to solve it. Indeed, the DRP is a multi-criteria problem. The proposed solution aims to reduce both route and duration in response to a certain quality of service provided. Actually, during the system of transport on demand (TOD) there are many problems that can inhibit the proper functioning of the system, such as failure of the car and bottling. In this work, we have contributed to the resolution of stochastic DRP in solving possible problems when touring vehicles such as bottling, vehicle failures, accidents and vehicle occupancy service. We have developed an approach which is based on the genetic algorithm kind of the kind NSGAII to optimize travel time and elapsed travel time during the shot, taking into account the possible rate of risk in this selected itinerary.