A new image dissimilarity measure incorporating human perception
- Authors: Shojanazeri, Hamid , Teng, Shyh , Aryal, Sunil , Zhang, Dengsheng , Lu, Guojun
- Date: 2018
- Type: Text , Unpublished work
- Full Text:
- Description: Pairwise (dis) similarity measure of data objects is central to many applications of image anlaytics, such as image retrieval and classification. Geometric distance, particularly Euclidean distance ((
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
A Hybrid data dependent dissimilarity measure for image retrieval
- Authors: Shojanazeri, Hamid , Teng, Shyh , Lu, Guojun
- Date: 2017
- Type: Text , Unpublished work
- Full Text:
- Description: Abstract— In image retrieval, an effective dissimilarity measure is required to retrieve the perceptually similar images. Minkowski-type (lp ) distance is widely used for image retrieval, however it has its limitations. 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. !! also favours the most dominant components in calculating the total dissimilarity. A data dependent measure, named !! -dissimilarity, which estimates the dissimilarity using the data distribution, has been proposed recently. Rather than relying on geometric distance, it measures the dissimilarity between two instances in each dimension as a probability mass in a region that encloses the two instances. It considers two instances in a sparse region to be more similar than in a dense region. Using the probability of data mass enables all the dimensions of feature vectors to contribute in the final estimate of dissimilarity, so it does not just heavily bias towards the most dominant components. However, relying only on data distribution and completely ignoring the geometric distance raise another limitation. This can result in finding two instances similar only due to being in a sparse region, however if the geometric distance between them is large then they are not perceptually similar. To address this limitation we proposed a new hybrid data dependent dissimilarity (HDDD) measure that considers both data distribution as well as geometric distance. Our experimental results using Corel database and Caltech 101 show that (HDDD) leads to higher image retrieval performance than lp distance (lpD) and mp.
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.