This paper introduces a new CBIR system based on two different approaches in order to achieve the retrieval efficiency and accuracy. Color and texture information is extracted and used in this work to form the feature vector. To do the texture feature extraction this system uses DCT and DCT Wavelet transform to generate the feature vectors of the query and database images. Color information ext...

This paper describes the use of histogram modification functions to improve the retrieval efficiency of the content-based image retrieval system based on bins approach. Four different functions explored in this paper used as histogram specification to modify the histograms are; histogram equalization (EQH), polynomial function (POLY), linear equations (LinearEQ1,2,3) and logarithmic function (L...

Most of the existing frequent item sets mining techniques are based up on Multimedia data mining. In this paper we propose a novel approach for frequent item sets mining using color, texture and shape. Frequent item set is an item set that satisfies minimum support. The data bases tested in the Multimedia Miner System is constructed. Each Image contains two descriptors: a feature descriptor and...

This paper proposes a novel colour independent Content Based Image Retrieval scheme. Important image information is extracted from visually important areas of image such as edges. Global image features are extracted from the relation among the detailed image information. These two groups of information generate the feature vector. The novel algorithm presented here is a two pass algorithm. Firs...

In this paper a system for continuous analysis, visualization and classification of musical streams is proposed. The system performs visualization and classification task by means of three high-level, semantic features extracted computing a reduction on a multidimensional low-level feature vector through the usage of Gaussian Mixture Models. The visualization of the semantic characteristics of ...

This paper describes how we maintain color and spatial index information on more than 1,000,000 images and how we allow users to browse the spatial color feature space. We break down all our images in color-based quad trees and we store all quad trees in our main-memory database. We allow users to browse the quad trees directly, or they can pre-select images through our color bit vector, which ...

The paper presents performance comparison of image retrieval methods based on texture feature extraction using Vector Quantization (VQ) codebook generation techniques like LBG and KEVR (Kekre‟s Error Vector Rotation) with assorted color spaces. The image is divided into non overlapping blocks of size 2x2 pixels (each pixel with red, green and blue component). Each block corresponds to one train...

Most of the content-based image retrieval systems focus on similarity-based retrieval of images by utilizing color, shape and texture features. For color-based image retrieval, the average color or color-histograms of images are widely used as feature vectors. In this paper, we propose a new searching scheme, called Fuzzy Membership Value-Indexing, to guarantee higher retrieval quality. This sc...

This work exposes the automatic computation system to analyse the cotton leaf spot diseases. First to initialize the images from the database (Image features) that are highly related to the test image (new image), where test image is given by the user. Three features are used for matching the train image features in database images, namely color feature variance, shape and texture feature varia...

The term CBIR refers to the process of retrieving similar images from a large collection of image database. The image retrieval is done on the basis of similarity matching between query image and database images. Different feature extraction techniques are being used to extract features of an image. The important image features are Color, Texture, Shape, Spatial location, edges etc. The CBIR (C...