Rapid semantic categorization of scenes: an advantage for emotional images?

Pan, Tiffany S; Sokolich, Anne T; Lin, Patricia; Abdel-Ghaffar, Samy; Bishop, Sonia J.

doi:10.1167/13.9.1312

Journal of Vision

2013

DOI: 10.1167/13.9.1312

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Rapid semantic categorization of scenes: an advantage for emotional images?

Tiffany S Pan

Anne T Sokolich

Patricia Lin

et al.

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“…However, these studies used only visual features to describe scenes, while ignored semantic features which represent special geographic information. Consequently, they are only appropriate to classify relatively homogeneous scenes in which the distributions of components are regular, rather than heterogeneous scenes such as satellite imageries [13]- [15]. In addition, some features are variant to affine transformation, such as rotation, translate, and scaling, which impedes scene classification.…”

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Semantic Classification of Heterogeneous Urban Scenes Using Intrascene Feature Similarity and Interscene Semantic Dependency

Zhang

Wang

2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Semantic classification of urban scenes aims to classify scenes composed of many different types of objects into predefined semantic classes. To learn the association between urban scenes and semantic classes, five tasks are needed: 1) segmenting the image into scenes; 2) establishing semantic classes of scenes; 3) extracting and transforming features; 4) measuring the intrascenes feature similarity; and 5) labeling each scene by a semantic classification method. Despite many efforts on these tasks, most existing works consider only visual features with inconsistent similarity measurement, while ignore semantic features inside scenes and the interactions between scenes, leading to poor classification results for high heterogeneous scenes. To solve these problems, this study combines intrascene feature similarity and interscene semantic dependency to form a two-step classification approach. For the first step, visual and semantic features are first optimized to be invariant to affine transformation, and then are employed in K-Nearest Neighbor to initially classify scenes. For the second step, multinomial distribution is presented to model both the spatial and semantic dependency between scenes, and then used to improve the initial classification results. The implementations conducted in two study areas indicate that the proposed approach produces better results for heterogeneous scenes than visual interpretation, as it can discover and model the hidden information between scenes which is often ignored by existing methods. In addition, compared with the initial classification, the optimized step improves accuracies by 3.6% and 5% in the two study areas, respectively.

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Semantic Classification of Heterogeneous Urban Scenes Using Intrascene Feature Similarity and Interscene Semantic Dependency

Zhang

Wang

2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Residential land extraction from high spatial resolution optical images using multifeature hierarchical method

Zhang

Liu

2019

J. Appl. Rem. Sens.

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Residential land extraction from high spatial resolution optical images using multifeature hierarchical method,"Abstract. Residential land (RL), as a typical kind of urban functional zone, plays an important role in urban planning and land census. Recent years have witnessed frequent changes in RL via the process of urbanization. The extraction of RL from high spatial resolution optical images can reflect the status quo of land use/land cover to a certain extent, which is of great significance to land census and urban planning. We adopt a scene classification strategy to extract RL and mainly focus on the extraction of four common types of RL in China: old-style village, lowdensity high-rise, medium-density low-rise, and low-density low-rise. We design a multifeature hierarchical (MFH) algorithm for RL extraction. First, RL is extracted based on the gray level concurrence matrix and a fuzzy classification algorithm. Then an improved bag-of-visual-words algorithm is introduced to further realize the extraction of RL. The effectiveness of our proposed method is analyzed with a sample dataset and large images. We also analyze the separability among different kinds of RL. We compare the experimental results with those of three other algorithms, and the results demonstrate that the MFH algorithm performs better in terms of the accuracy and efficiency of the RL extraction. The results can provide services for land surveying and urban planning, and the technological processes and experimental design in the algorithm can provide a reference for the research in related fields. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Rapid semantic categorization of scenes: an advantage for emotional images?

Cited by 2 publications

References 0 publications

Semantic Classification of Heterogeneous Urban Scenes Using Intrascene Feature Similarity and Interscene Semantic Dependency

Semantic Classification of Heterogeneous Urban Scenes Using Intrascene Feature Similarity and Interscene Semantic Dependency

Residential land extraction from high spatial resolution optical images using multifeature hierarchical method

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