Pattern recognition technologies for multimedia information processing

Abstract

Advanced algorithms based on pattern recognition for segmentation, recognition, and indexing of image, video, speech and audio data have been investigated and developed to analyze multimedia information, recently. Pattern recognition algorithms generally aim to provide a reasonable answer for all possible inputs and to perform most likely matching of the inputs, taking into account their statistical variation. Theses algorithms have been widely used in many applications to recognize what people understand such as objects, faces, human actions, and so on. Although there has been a great progress in fields of image and speech processing, virtual and augmented reality, human-computer interaction, and so on, we still have the difficulties of implementing general models that are robust to real-world variations. In this special issue, our goal is to consolidate the recent research achievements that include diverse pattern recognition based information processing for multimedia applications.

A Genetic Algorithm with Lower Neighborhood Search for the Three-Dimensional Multiorder Open-Size Rectangular Packing Problem

“…Te problem was proven to be NP-hard and exact algorithms were time-consuming [5,6]. Research transitioned to metaheuristics, and as the 21st century progressed, the focus shifted to multicontainer [7], dynamic packing [8], and machine learning methodologies [9]. Recent advancements have led to specialized cases such as open-dimension packing [10] and multiorder mixed packing problems [11].…”