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Improved kernel descriptors for effective and efficient image classification

- Karmakar, Priyabrata, Teng, Shyh, Zhang, Dengsheng, Liu, Ying, Lu, Guojun

  • Authors: Karmakar, Priyabrata , Teng, Shyh , Zhang, Dengsheng , Liu, Ying , Lu, Guojun
  • Date: 2017
  • Type: Text , Conference proceedings
  • Relation: 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA); Sydney, Australia; 29th November-1st December 2017 p. 195-202
  • Full Text: false
  • Reviewed:
  • Description: Kernel descriptors have been proven to outperform existing histogram based local descriptors as such descriptors are extracted from the match kernels which measure similarities between image patches using different pixel attributes (gradient, colour or LBP pattern). The extraction of kernel descriptors does not require coarse quantization of pixel attributes. Instead, each pixel equally participates in matching between two image patches. In this paper, by leveraging the kernel properties, we propose a unique approach which simultaneously increases the effectiveness and efficiency of the existing kernel descriptors. Specifically, this is done by improving the similarity measure between two different patches in terms of any pixel attribute. The proposed kernel descriptors are more discriminant, take less time to be extracted and have much lower dimensions. Our experiments on Scene Categories and Caltech 101 databases show that our proposed approach outperforms the existing kernel descriptors.

Improved Tamura features for image classification using kernel based descriptors

- Karmakar, Priyabrata, Teng, Shyh, Zhang, Dengsheng, Liu, Ying, Lu, Guojun

  • Authors: Karmakar, Priyabrata , Teng, Shyh , Zhang, Dengsheng , Liu, Ying , Lu, Guojun
  • Date: 2017
  • Type: Text , Conference proceedings
  • Relation: 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA); Sydney, Australia; 29th November-1st December 2017 p. 461-467
  • Full Text: false
  • Reviewed:
  • Description: Tamura features are based on human visual perception and have huge potential in image representation. Conventional Tamura features only work on homogeneous texture images and perform poor on generic images. Therefore, many researchers attempt to improve Tamura features and most of the improvements are based on histogram based representation. Kernel descriptors have been shown to outperform existing histogram based local features as such descriptors do not require coarse quantization of pixel attributes. Instead, in kernel descriptor framework, each pixel equally participates in matching between two image patches. In this paper, we propose a set of kernel descriptors that are based on Tamura features. Additionally, the proposed descriptors are invariant to local rotations. Experimental results show that our proposed approach outperforms the conventional Tamura features significantly.

Integrating object ontology and region semantic template for crime scene investigation image retrieval

- Liu, Ying, Huang, Yuan, Zhang, Shuai, Zhang, Dengsheng, Ling, Nam

  • Authors: Liu, Ying , Huang, Yuan , Zhang, Shuai , Zhang, Dengsheng , Ling, Nam
  • Date: 2017
  • Type: Text , Conference proceedings
  • Relation: 2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA); Siem Reap, Cambodia; 18th-20th June 2017 p. 149-153
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  • Reviewed:
  • Description: Crime Scene Investigation (CSI) image retrieval plays an important role in solving crimes by providing useful clues for the police force. However, there has been little work done in this area due to limited public data access by researchers. Tested on real-world CSI images, it was observed that existing content-based image retrieval (CBIR) methods do not necessarily retrieve as effectively on CSI image database as compared to other general image databases. Hence, it is important to design CBIR algorithm tuned to CSI image database. This paper proposes a region-based semantic learning method based on object ontology which associates image categories with 'objects' in CSI images. Each object corresponds to a pre-defined semantic template (ST) which is defined as the average color and texture feature of a set of sample regions. In this way, low-level features of each region in a CSI image can be converted to an 'object' by comparing the region features with the set of pre-defined STs. The 'objects' in an image categorize the image based on the object ontology. The above process is referred to as 'On-Set'. To further improve retrieval performance of On-Set, a weighting strategy named object-frequency-based weighting (OFW) is designed inspired by the idea of term frequency-inverse document frequency (TF-IDF). In OFW, heavier weight is assigned to regions that appear more often in one class and less often in other classes. Experimental results on real-world image data proved the effectiveness of the proposed method for CSI image database retrieval.

Multi-feature fusion for Crime Scene Investigation image retrieval

- Liu, Ying, Hu, Dan, Fan, Jiulun, Wang, Fuping, Zhang, Dengsheng

Combining pyramid match kernel and spatial pyramid for image classification

- Karmakar, Priyabrata, Teng, Shyh, Zhang, Dengsheng, Lu, Guojun, Liu, Ying

Yield and characteristics of pyrolysis products obtained from Schizochytrium limacinum under different temperature regimes

- Li, Gang, Zhou, Yuguang, Ji, Fang, Liu, Ying, Adhikari, Benu, Tian, Li, Ma, Zonghu, Dong, Renjie

  • Authors: Li, Gang , Zhou, Yuguang , Ji, Fang , Liu, Ying , Adhikari, Benu , Tian, Li , Ma, Zonghu , Dong, Renjie
  • Date: 2013
  • Type: Text , Journal article
  • Relation: Energies Vol. 6, no. 7 (2013), p. 3339-3352
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  • Description: Pyrolysis-gas chromatographic mass spectrometry (Py-GC/MS) was used to determine the yield and chemical composition of the pyrolysis products of Schizochytrium limacinum. The pyrolysis was carried out by varying the temperature from 300 °C to 800 °C. It was found that the main decomposition temperature of Schizochytrium limacinum was 428.16 °C, at which up to 66.5% of the mass was lost. A further 18.7% mass loss then occurred in a relatively slow pace until 760.2 °C due to complete decomposition of the ash content of Schizochytrium limacinum. The pyrolysis of Schizochytrium limacinum at 700 °C produced the maximum yield (67.7%) of pyrolysis products compared to 61.2% at 400 °C. While pollutants released at 700 °C (12.3%) was much higher than that of 400 °C (2.1%). Higher temperature will lead to more pollutant (nitrogen compounds and PAHs) release,which is harmful to the environment. Considering the reasonably high yield and minimum release of pollutants, a lower pyrolysis temperature (400 °C) was found to be optimum for producing biofuel from Schizochytrium limacinum.
  • Description: C1

Digital image retrieval using intermediate semantic features and multistep search

- Zhang, Dengsheng, Liu, Ying, Hou, Jin

  • Authors: Zhang, Dengsheng , Liu, Ying , Hou, Jin
  • Date: 2008
  • Type: Text , Conference paper
  • Relation: Proceedings of the Digital Image Computing: Techniques and Applications p. 513-518
  • Full Text: false
  • Reviewed:
  • Description: Recently, semantic image retrieval has attracted large amount of interest due to the rapid growth of digital image storage. However, existing approaches have severe limitations. In this paper, a new approach to digital image retrieval using intermediate semantic features and multistep search has been proposed. Instead of looking for human level semantics which is too challenging at this stage, the research looks for heuristic information and intermediate semantic features which can describe image content objectively. Different from the conventional approaches, the intermediate features are used as filters to eliminate large amount of irrelevant images. Conventional content based image retrieval techniques and relevance feedback (RF) are applied following the filtering to improve the retrieval accuracy. The proposed system has the power of capturing both regional features and global features, and making use of both semantic features and low level features. The proposed system also uses a powerful user interface to provide users with convenient retrieval mechanisms including SQL, RF and query by example. Results show the system has a significant gain over existing region based and global image retrieval approaches

Region-based image retrieval with high-level semantics using decision tree learning

- Liu, Ying, Zhang, Dengsheng, Lu, Guojun

  • Authors: Liu, Ying , Zhang, Dengsheng , Lu, Guojun
  • Date: 2008
  • Type: Text , Journal article
  • Relation: Pattern Recognition Vol. 41, no. 8 (2008), p. 2554-2570
  • Full Text: false
  • Reviewed:
  • Description: Semantic-based image retrieval has attracted great interest in recent years. This paper proposes a region-based image retrieval system with high-level semantic learning. The key features of the system are: (1) it supports both query by keyword and query by region of interest. The system segments an image into different regions and extracts low-level features of each region. From these features, high-level concepts are obtained using a proposed decision tree-based learning algorithm named DT-ST. During retrieval, a set of images whose semantic concept matches the query is returned. Experiments on a standard real-world image database confirm that the proposed system significantly improves the retrieval performance, compared with a conventional content-based image retrieval system. (2) The proposed decision tree induction method DT-ST for image semantic learning is different from other decision tree induction algorithms in that it makes use of the semantic templates to discretize continuous-valued region features and avoids the difficult image feature discretization problem. Furthermore, it introduces a hybrid tree simplification method to handle the noise and tree fragmentation problems, thereby improving the classification performance of the tree. Experimental results indicate that DT-ST outperforms two well-established decision tree induction algorithms ID3 and C4.5 in image semantic learning.
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