Thermal Infrared Hyperspectral Dimension Reduction Experiment Results For Global And Local Information Based Linear Discriminant Analysis

Thermal infrared hyperspectral image processing has become an important research topic in remote sensing. One of the research topics in thermal infrared hyperspectral image classification is dimension reduction. In this paper, thermal infrared hyperspectral dimension reduction experiment results for global and local information based linear discriminant analysis is presented. Advantages of the use of not only global pattern information, but also local pattern information are tested in thermal infrared hyperspectral image processing

Dimension reduction using global and local pattern information-based maximum margin criterion

Dimension reduction is an important research area in pattern recognition when dealing with high-dimensional data. In this paper, a novel supervised dimension reduction approach is introduced for classification. Advantages of using not only global pattern information but also local pattern information are examined in the maximum margin criterion framework. Experimental comparative results in object recognition, handwritten digit recognition, and hyperspectral image classification are presented. According to the experimental results, the proposed method can be a valuable choice for dimension reduction when considering the difficulty of obtaining training samples for some applications

Semi-supervised dimension reduction approaches integrating global and local pattern information

Dimension reduction is an important research area in pattern recognition. Use of both supervised and unsupervised data can be an advantage in the case of lack of labeled training data. Moreover, use of both global and local pattern information can contribute classification performances. Therefore, four important primary components are essential to design a well-performed semi-supervised dimension reduction approach: global pattern modeling by a supervised manner, local pattern modeling by a supervised manner, global pattern modeling by an unsupervised manner, and local pattern modeling by an unsupervised manner. These primary components are integrated into two proposed methods. The first is the semi-supervised global-local linear discriminant analysis, and the second is the semi-supervised global-local maximum margin criterion. The proposed methods are examined in object recognition and hyperspectral image classification. According to the experimental results, the promising results are obtained against to comparative semi-supervised methods

A Graph-Based Approach for Video Scene Detection

In this paper, a graph-based method for video scene detection is proposed. The method is based on a weighted undirected graph. Each shot is a vertex on the graph. Edge weights among the vertices are evaluated by using spatial and temporal similarities of shots. By using the complete information of the graph, a set of the vertices mostly similar to each other and dissimilar to the others is detected. Temporal continuity constraint is achieved on this set. This set is the first detected video scene. The vertices of the video scene are extracted from the graph and the process is repeated by a certain number. The certain number of the video scenes whose boundaries are determined are placed in the temporal domain. Each temporal pail between two detected scenes is accepted as a video scene

Graph-based multilevel temporal video segmentation

This paper presents a graph-based multilevel temporal video segmentation method. In each level of the segmentation, a weighted undirected graph structure is implemented. The graph is partitioned into clusters which represent the segments of a video. Three low-level features are used in the calculation of temporal segments' similarities: visual content, motion content and shot duration. Our strength factor approach contributes to the results by improving the efficiency of the proposed method. Experiments show that the proposed video scene detection method gives promising results in order to organize videos without human intervention

VIDEO SCENE DETECTION USING DOMINANT SETS

In this paper, we propose a weighted undirected graph-based video scene detection method. The method is based on the idea of using the complete information of the graph. For this aim, each shot is represented by a vertex on the graph. Edge weights among vertices are evaluated by using spatial and temporal similarities of shots. Only a single video scene boundary which has the highest probability to be the correct one is determined and this scene boundary information is also used as a clue in the next steps. A tree-based peeling strategy is proposed to determine the boundaries of the remaining scenes. In order to test our graph-based video scene detection method, we used DVD chapters' information and promising results were obtained when compared to the results of the similar work presented in literature

Graph-based multilevel temporal segmentation of scripted content videos

This paper concentrates on a graph-based multilevel temporal segmentation method for scripted content videos. In each level of the segmentation, a similarity matrix of frame strings, which are series of consecutive video frames, is constructed by using temporal and spatial contents of frame strings. A strength factor is estimated for each frame string by using a priori information of a scripted content. According to the similarity matrix reevaluated from a strength function derived by the strength factors, a weighted undirected graph structure is implemented. The graph is partitioned to clusters, which represent segments of a video. The resulting structure defines a hierarchically segmented video tree. Comparative performance results of different types of scripted content videos are demonstrated

Video Content Analysis Using Dominant Sets

In this paper, a graph-based method for video content analysis is proposed The characteristics of the detected shots are investigated for news, commercial, animated cartoon, basketball and documentary videos and experimental studies are realized on these videos. The maximum clique on the weighted and undirected graph, which is constructed according to visual content, is tried being detected. it is inferred that specially in news and commercials, the proposed method can be used for temporal video segmentation

Unsharp masking filter based shadow-invariant feature extraction for hyperspectral signatures

Hyperspectral image processing is an important research topic in remote sensing. The effect of atmosphere, especially cloud shadows need to be taken care of in hyperspectral image processing analysis. In this paper, a shadow-invariant feature extraction technique based on unsharp mask filtering is proposed for hyperspectral signatures. This technique is designed to remedy the problem of one material having different spectral signatures due to illumination condition. Similarity of the two signatures belonging to a same material in differently illuminated areas (shadow and non-shadow) is investigated before and after filtering. According to the first experiments, the proposed approach seems to be effective