Content-based image retrieval with the normalized information distance

Gondra, Iker; Heisterkamp, Douglas R.

doi:10.1016/j.cviu.2007.11.001

Cited by 23 publications

(10 citation statements)

References 43 publications

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“…Euclidean Distanceis the distance which is used to calculate the distance between two points (p,q) in any dimension of space and it is one of the most commonly used distance measures (Iker et al, 2008), (Mario et al, 2008). Distance between features of the query image and the image within the closest clusters is determined.…”

Section: Algorithm For Rule Generationmentioning

confidence: 99%

CBIR: Primary Collection of Relevant Image Selection using Rule Based Boolean Query

2016

Rev. Téc. Ing. Univ. Zulia

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Content Based Image Retrieval is an active way of searching, browsing, or retrieving images from a large image repository. Researchers are intensely competing for developing efficient and precise image retrieval tool which can be applied to various image searching devices that run on different platforms. By applying static clustering, similarity between query image and each cluster is determined to find the closest cluster which has greater likelihood. To obtain more relevant images, it searches not only a single cluster but also the nearby clusters and when the database size increases, all the clusters get updated to reflect the changes in the database. To overcome this problem the proposed method develops an efficient CBIR system that uses rule based Boolean query. It does not generate clusters, instead a dynamic rule has been derived using query image features to retrieve the primary collection of relevant images which has higher semantics. Instead of using dissimilarity distances directly this system determines the subset of images and then it is refined to yield the topmost closest images by finding the shortest distance using Euclidean distance. Gray-level co-occurrence matrix is used to determine texture features such as energy, entropy, contrast and homogeneity. Region based method is used for shape features such as area, perimeter, solidity and circularity of a largest connected component. Dominant color descriptors in RGB space are used for extracting the color feature. Performance can be compared to an existing system by evaluating the time, retrieval precision and recall. The experiment is performed on Corel dataset and it shows superior performance than the previous system.

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Section: Algorithm For Rule Generationmentioning

confidence: 99%

CBIR: Primary Collection of Relevant Image Selection using Rule Based Boolean Query

2016

Rev. Téc. Ing. Univ. Zulia

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“…Theoretically, this makes NCD a suitable metric for Content-Based Image Retrieval (CBIR) which attempts to find similar images based on a query image's content. The application of NCD for CBIR in [7] has shown to produce statistically significant dissimilarity measures when tested against a null hypothesis of random retrieval. The NCD between images was used as a metric to search the visual content encoded in the raw data directly, thus bypassing feature selection and weighting.…”

Section: Introductionmentioning

confidence: 99%

“…In [7] the compressed size of the concatenation of x and y is used to estimate ( ) as well as ( ). Compressors search for sequences shared between x and y in order to reduce the redundancy in the concatenated sequences.…”

Section: Introductionmentioning

confidence: 99%

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Effect of Image Linearization on Normalized Compression Distance

Mortensen

Wu²,

Furst

et al. 2009

Communications in Computer and Information Science

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Abstract. Normalized Information Distance, based on Kolmogorov complexity, is an emerging metric for image similarity. It is approximated by the Normalized Compression Distance (NCD) which generates the relative distance between two strings by using standard compression algorithms to compare linear strings of information. This relative distance quantifies the degree of similarity between the two objects. NCD has been shown to measure similarity effectively on information which is already a string: genomic string comparisons have created accurate phylogeny trees and NCD has also been used to classify music. Currently, to find a similarity measure using NCD for images, the images must first be linearized into a string, and then compared. To understand how linearization of a 2D image affects the similarity measure, we perform four types of linearization on a subset of the Corel image database and compare each for a variety of image transformations. Our experiment shows that different linearization techniques produce statistically significant differences in NCD for identical spatial transformations.

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“…The content based image retrieval (CBIR) approach challenge is how to fill the gap between the low level features that describe the scenes and our human understandable semantic concepts. This gap of understanding is called the semantic gap [5] [6]. In addition, these semantic concepts themselves may be defined differently, e.g.…”

Section: Introductionmentioning

confidence: 99%

Spatial Cloud Detection and Retrieval System for Satellite Images

Laban¹,

Nasr²,

El-Saban³

et al. 2012

IJACSA

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Abstract-In last the decade we witnessed a large increase in data generated by earth observing satellites. Hence, intelligent processing of the huge amount of data received by hundreds of earth receiving stations, with specific satellite image oriented approaches, presents itself as a pressing need. One of the most important steps in earlier stages of satellite image processing is cloud detection. Satellite images having a large percentage of cloud cannot be used in further analysis. While there are many approaches that deal with different semantic meaning, there are rarely approaches that deal specifically with cloud detection and retrieval. In this paper we introduce a novel approach that spatially detect and retrieve clouds in satellite images using their unique properties .Our approach is developed as spatial cloud detection and retrieval system (SCDRS) that introduce a complete framework for specific semantic retrieval system. It uses a Query by polygon (QBP) paradigm for the content of interest instead of using the more conventional rectangular query by image approach. First, we extract features from the satellite images using multiple tile sizes using spatial and textural properties of cloud regions. Second, we retrieve our tiles using a parametric statistical approach within a multilevel refinement process. Our approach has been experimentally validated against the conventional ones yielding enhanced precision and recall rates in the same time it gives more precise detection of cloud coverage regions.

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Content-based image retrieval with the normalized information distance

Cited by 23 publications

References 43 publications

CBIR: Primary Collection of Relevant Image Selection using Rule Based Boolean Query

CBIR: Primary Collection of Relevant Image Selection using Rule Based Boolean Query

Effect of Image Linearization on Normalized Compression Distance

Spatial Cloud Detection and Retrieval System for Satellite Images

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