A kernel-based approach for content-based image retrieval
- Authors: Karmakar, Priyabrata , Teng, Shyh , Lu, Guojun , Zhang, Dengsheng
- Date: 2018
- Type: Text , Conference proceedings , Conference paper
- Relation: 2018 International Conference on Image and Vision Computing New Zealand; Auckland, New Zealand; 19th-21st November 2018 p. 1-6
- Full Text: false
- Reviewed:
- Description: Content-based image retrieval (CBIR) is a popular approach to retrieve images based on a query. In CBIR, retrieval is executed based on the properties of image contents (e.g. gradient, shape, color, texture) which are generally encoded into image descriptors. Among the various image descriptors, histogram-based descriptors are very popular. However, they suffer from the limitation of coarse quantization. In contrast, the use of kernel descriptors (KDES) is proven to be more effective than histogram-based descriptors in other applications, e.g. image classification. This is because, in the KDES framework, instead of the quantization of pixel attributes, each pixel equally takes part in the similarity measurement between two images. In this paper, we propose an approach for how the conventional KDES and its improved version can be used for CBIR. In addition, we have provided a detailed insight into the effectiveness of improved kernel descriptors. Finally, our experiment results will show that kernel descriptors are significantly more effective than histogram-based descriptors in CBIR.
A novel perceptual dissimilarity measure for image retrieval
- Authors: Shojanazeri, Hamid , Zhang, Dengsheng , Teng, Shyh , Aryal, Sunil , Lu, Guojun
- Date: 2018
- Type: Text , Conference proceedings , Conference paper
- Relation: 2018 International Conference on Image and Vision Computing New Zealand, IVCNZ 2018; Auckland, New Zealand; 19th-21st November 2018 Vol. 2018-November, p. 1-6
- Full Text: false
- Reviewed:
- Description: Similarity measure is an important research topic in image classification and retrieval. Given a type of image features, a good similarity measure should be able to retrieve similar images from the database while discard irrelevant images from the retrieval. Similarity measures in literature are typically distance based which measure the spatial distance between two feature vectors in high dimensional feature space. However, this type of similarity measures do not have any perceptual meaning and ignore the neighborhood influence in the similarity decision making process. In this paper, we propose a novel dissimilarity measure, which can measure both the distance and perceptual similarity of two image features in feature space. Results show the proposed similarity measure has a significant improvement over the traditional distance based similarity measure commonly used in literature.
- Description: International Conference Image and Vision Computing New Zealand
Enhanced colour image retrieval with cuboid segmentation
- Authors: Murshed, Manzur , Karmakar, Priyabrata , Teng, Shyh , Lu, Guojun
- Date: 2018
- Type: Text , Conference proceedings , Conference paper
- Relation: 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018; Canberra, Australia; 10th-13th December 2018
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- Description: In this paper, we further investigate our recently proposed cuboid image segmentation algorithm for effective image retrieval. Instead of using all cuboids (i.e. segments), we have proposed two approaches to choose different subsets of cuboids appropriately. With the experimental results on eBay dataset, we have shown that our proposals outperform retrieval performance of the existing technique. In addition, we have investigated how many segments are required for the most effective image retrieval and provide a quick method to determine the suitable number of cuboids.
- Description: 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018
A hybrid data dependent dissimilarity measure for image retrieval
- Authors: Shojanazeri, Hamid , Teng, Shyh , Zhang, Dengsheng , 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. 141-148
- Full Text: false
- Reviewed:
- Description: In image retrieval, an effective dissimilarity (or similarity) measure is required to retrieve the perceptually similar images. Minkowski-type distance is widely used for image retrieval, however it has its limitation. It focuses on distance between image features and ignores the data distribution of the image features, which can play an important role in measuring perceptual similarity of images. To address this limitation, a data dependent measure named m-p, which calculates the dissimilarity using the data distribution rather than geometric distance has been proposed recently. It considers two instances in a sparse region to be more similar than in a dense region. Relying only on data distribution and completely ignoring the geometric distance raise other limitations. This may result in finding two perceptually dissimilar instances similar due to being located in a sparse region or vice versa. We proposed a new hybrid dissimilarity measure and experimental results show that it addresses these limitations.
Colour image annotation using hybrid intelligent techniques for image retrieval
- Authors: Kulkarni, Siddhivinayak , Kulkarni, Pradnya
- Date: 2012
- Type: Text , Conference proceedings
- Full Text:
- Description: This paper presents a novel technique for colour image annotation based on neural networks and fuzzy logic. Neural network is proposed for classifying the images based on their contents and fuzzy logic is proposed for interpreting the content of an image in terms of natural language. One of the main aspects of this research is to avoid re-training of the neural networks by training the content of the image. Neural network is not trained on database of images; therefore image can be added or deleted from image database without affecting the training. The proposed hybrid technique is tested on real world colour image dataset and promising results are obtained. © 2012 IEEE.
- Description: 2003010700
Hybrid technique for colour image classification and efficient retrieval based on fuzzy logic and neural networks
- Authors: Fernando, Ranisha , Kulkarni, Siddhivinayak
- Date: 2012
- Type: Text , Conference proceedings
- Full Text:
- Description: Developments in the technology and the Internet have led to increase in number of digital images and videos. Thousands of images are added to WWW every day. To retrieve the specific images efficiently from database or from Internet is becoming a challenge now a day. As a result, the necessity of retrieving images has emerged to be important to various professional areas. This paper proposes a novel fuzzy approach to classify the colour images based on their content, to pose a query in terms of natural language and fuse the queries based on neural networks for fast and efficient retrieval. Number of experiments was conducted for classification and retrieval of images on sets of images and promising results were obtained. The results were analysed and compared with other similar image retrieval system. © 2012 IEEE.
MapReduce neural network framework for efficient content based image retrieval from large datasets in the cloud
- Authors: Venkatraman, Sitalakshmi , Kulkarni, Siddhivinayak
- Date: 2012
- Type: Text , Conference proceedings
- Full Text:
- Description: Recently, content based image retrieval (CBIR) has gained active research focus due to wide applications such as crime prevention, medicine, historical research and digital libraries. With digital explosion, image collections in databases in distributed locations over the Internet pose a challenge to retrieve images that are relevant to user queries efficiently and accurately. It becomes increasingly important to develop new CBIR techniques that are effective and scalable for real-time processing of very large image collections. To address this, the paper proposes a novel MapReduce neural network framework for CBIR from large data collection in a cloud environment. We adopt natural language queries that use a fuzzy approach to classify the colour images based on their content and apply Map and Reduce functions that can operate in cloud clusters for arriving at accurate results in real-time. Preliminary experimental results for classifying and retrieving images from large data sets were quite convincing to carry out further experimental evaluations. © 2012 IEEE.
- Description: 2003010699