Development of Manufacturing Cells Using an Artificial Ant-Based Algorithm with Different Similarity Coefficients

Although there exists several ways of solving the cellular manufacturing problem, including several ant-based algorithms, many of these algorithms focus on obtaining the best possible answer instead of efficiency. An existing artificial-ant based algorithm AntClass, was modified so that it is easier to manipulate. AntClass uses Euclidean vectors to measure the similarity between parts, because similarity is used to group parts together instead of distances, the modified version uses similarity coefficients. The concept of heaping clusters was also introduced to ant algorithms for cellular manufacturing. Instead of using Euclidean vectors to measure the distance to the center of a heap, as in the AntClass algorithm, an average similarity was introduced to measure the similarity between a part and a heap. The algorithm was tested on five common similarity coefficients to determine the similarity coefficient which gives the better quality solution and the most efficient process

Edge Detection by Cost Minimization

Edge detection is cast as a problem in cost minimization. This is achieved by the formulation of two cost functions which evaluate the quality of edge configurations. The first is a comparative cost function (CCF), which is a linear sum of weighted cost factors. It is heuristic in nature and can be applied only to pairs of similar edge configurations. It measures the relative quality between the configurations. The detection of edges is accomplished by a heuristic iterative search algorithm which uses the CCF to evaluate edge quality. The second cost function is the absolute cost function (ACF), which is also a linear sum of weighted cost factors. The cost factors capture desirable characteristics of edges such as accuracy in localization, thinness, and continuity. Edges are detected by finding the edge configurations that minimize the ACF. We have analyzed the function in terms of the characteristics of the edges in minimum cost configurations. These characteristics are directly dependent of the associated weight of each cost factor. Through the analysis of the ACF, we provide guidelines on the choice of weights to achieve certain characteristics of the detected edges. Minimizing the ACF is accomplished by the use of Simulated Annealing. We have developed a set of strategies for generating next states for the annealing process. The method of generating next states allows the annealing process to be executed largely in parallel. Experimental results are shown which verify the usefulness of the CCF and ACF techniques for edge detection. In comparison, the ACF technique produces better edges than the CCF or other current detection techniques

Unsupervised Graph-based Rank Aggregation for Improved Retrieval

This paper presents a robust and comprehensive graph-based rank aggregation approach, used to combine results of isolated ranker models in retrieval tasks. The method follows an unsupervised scheme, which is independent of how the isolated ranks are formulated. Our approach is able to combine arbitrary models, defined in terms of different ranking criteria, such as those based on textual, image or hybrid content representations. We reformulate the ad-hoc retrieval problem as a document retrieval based on fusion graphs, which we propose as a new unified representation model capable of merging multiple ranks and expressing inter-relationships of retrieval results automatically. By doing so, we claim that the retrieval system can benefit from learning the manifold structure of datasets, thus leading to more effective results. Another contribution is that our graph-based aggregation formulation, unlike existing approaches, allows for encapsulating contextual information encoded from multiple ranks, which can be directly used for ranking, without further computations and post-processing steps over the graphs. Based on the graphs, a novel similarity retrieval score is formulated using an efficient computation of minimum common subgraphs. Finally, another benefit over existing approaches is the absence of hyperparameters. A comprehensive experimental evaluation was conducted considering diverse well-known public datasets, composed of textual, image, and multimodal documents. Performed experiments demonstrate that our method reaches top performance, yielding better effectiveness scores than state-of-the-art baseline methods and promoting large gains over the rankers being fused, thus demonstrating the successful capability of the proposal in representing queries based on a unified graph-based model of rank fusions

Texture and Colour in Image Analysis

Research in colour and texture has experienced major changes in the last few years. This book presents some recent advances in the field, specifically in the theory and applications of colour texture analysis. This volume also features benchmarks, comparative evaluations and reviews