As one of the most classic fields in computer vi- sion, image categorization has attracted widespread interests. Numerous algorithms have been proposed in the community, and many of them have advanced the state-of-the...As one of the most classic fields in computer vi- sion, image categorization has attracted widespread interests. Numerous algorithms have been proposed in the community, and many of them have advanced the state-of-the-art. How- ever, most existing algorithms are designed without consider- ation for the supply of computing resources. Therefore, when dealing with resource constrained tasks, these algorithms will fail to give satisfactory results. In this paper, we provide a comprehensive and in-depth introduction of recent develop- ments of the research in image categorization with resource constraints. While a large portion is based on our own work, we will also give a brief description of other elegant algo- rithms. Furthermore, we make an investigation into the re- cent developments of deep neural networks, with a focus on resource constrained deep nets.展开更多
Image categorization in massive image database is an important problem. This paper proposes an approach for image categorization, using sparse set of salient semantic information and hierarchy semantic label tree （H...Image categorization in massive image database is an important problem. This paper proposes an approach for image categorization, using sparse set of salient semantic information and hierarchy semantic label tree （HSLT） model. First, to provide more critical image semantics, the proposed sparse set of salient regions only at the focuses of visual attention instead of the entire scene was formed by our proposed saliency detection model with incorporating low and high level feature and Shotton＇s semantic texton forests （STFs） method. Second, we also propose a new HSLT model in terms of the sparse regional semantic information to automatically build a semantic image hierarchy, which explicitly encodes a general to specific image relationship. And last, we archived image dataset using image hierarchical semantic, which is help to improve the performance of image organizing and browsing. Extension experimefital results showed that the use of semantic hierarchies as a hierarchical organizing frame- work provides a better image annotation and organization, improves the accuracy and reduces human＇s effort.展开更多
基金This research was supported by the National Natural Science Foundation of China （Grant No. 61422203）.
文摘As one of the most classic fields in computer vi- sion, image categorization has attracted widespread interests. Numerous algorithms have been proposed in the community, and many of them have advanced the state-of-the-art. How- ever, most existing algorithms are designed without consider- ation for the supply of computing resources. Therefore, when dealing with resource constrained tasks, these algorithms will fail to give satisfactory results. In this paper, we provide a comprehensive and in-depth introduction of recent develop- ments of the research in image categorization with resource constraints. While a large portion is based on our own work, we will also give a brief description of other elegant algo- rithms. Furthermore, we make an investigation into the re- cent developments of deep neural networks, with a focus on resource constrained deep nets.
基金Acknowledgements This work was supported by National Natural Science Foundation of China （Grant Nos. 61272258, 61170124, 61170020, 61070223）, and Application Foundation Research Plan of Suzhou City, China （SYG201116）.
文摘Image categorization in massive image database is an important problem. This paper proposes an approach for image categorization, using sparse set of salient semantic information and hierarchy semantic label tree （HSLT） model. First, to provide more critical image semantics, the proposed sparse set of salient regions only at the focuses of visual attention instead of the entire scene was formed by our proposed saliency detection model with incorporating low and high level feature and Shotton＇s semantic texton forests （STFs） method. Second, we also propose a new HSLT model in terms of the sparse regional semantic information to automatically build a semantic image hierarchy, which explicitly encodes a general to specific image relationship. And last, we archived image dataset using image hierarchical semantic, which is help to improve the performance of image organizing and browsing. Extension experimefital results showed that the use of semantic hierarchies as a hierarchical organizing frame- work provides a better image annotation and organization, improves the accuracy and reduces human＇s effort.
