Content based image retrieval (CBIR) is an effective method of retrieving images from large image resources. CBIR is a technique in which images are indexed by extracting their low level features like, color, texture, shape, and spatial location, etc. Effective and efficient feature extraction mechanisms are required to improve existing CBIR performance. This paper presents a novel approach of ...

This paper presents a comparative approach for Content Based Image Retrieval (CBIR) using Scale Invariant Feature Transform (SIFT) algorithm and Principal Component Analysis (PCA) for color images. The motivation to use SIFT algorithm for CBIR is due to the fact that SIFT is invariant to scale, rotation and translation as well as partially invariant to affine distortion and illumination changes...

The rapid growth in the number of large-scale repositories has brought the need for efficient and effective content-based image retrieval (CBIR) systems. The state of the art in the CBIR systems is to search images in database that are “close” to the query image using some similarity measure. The current CBIR systems capture image features that represent properties such as color, texture, and/o...

Standardised image databases or rather the lack of them are one of the main weaknesses in the field of content based image retrieval (CBIR). Authors often use their own images or do not specify the source of their datasets. Naturally this makes comparison of results somewhat difficult. While a first approach towards a common colour image set has been taken by the MPEG 7 committee their database...

Image representation is critical to the successful realisation of Content Based Retrieval (CBIR) systems. The choice features represent image affects retrieval performance. Nowadays, databases are heterogeneous, and different feature types can be used for appropriate descriptions. This paper proposes an CBIR that combines Stacked Colour Histogram (SCH) Conventional (CCH) improve precision. pres...

This article examines the process of Content-based Image Retrieval (CBIR ) system , features and steps involved, methods employed, applications. CBIR aims to identify, sort, manage images on basis requirements posed system. The cross checks using Machine learning processes against a given database reduces manual effort sifting through all pictures individually. Incorporating references differen...

Content-based image retrieval (CBIR) with global features is notoriously noisy, especially for image queries with low percentages of relevant images in a collection. Moreover, CBIR typically ranks the whole collection, which is inefficient for large databases. We experiment with a method for image retrieval from multimedia databases, which improves both the effectiveness and efficiency of tradi...