Efficient and effective querying by image content
Journal of Intelligent Information Systems - Special issue: advances in visual information management systems
Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exploiting the JPEG Compression Scheme for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
VisualSEEk: a fully automated content-based image query system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Communications of the ACM
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scalable Color Image Indexing and Retrieval Using Vector Wavelets
IEEE Transactions on Knowledge and Data Engineering
Region-based image retrieval using integrated color, shape, and location index
Computer Vision and Image Understanding - Special issue on color for image indexing and retrieval
Image indexing using moments and wavelets
IEEE Transactions on Consumer Electronics
Color image indexing using BTC
IEEE Transactions on Image Processing
Image retrieval using BDIP and BVLC moments
IEEE Transactions on Circuits and Systems for Video Technology
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Rapid progress of multimedia and computing application has brought explosive growth in digital images in computer systems and in networks. Similarity matching is one of the important tasks in Content Based Image Retrieval (CBIR). Similarity matching requires distance computation of feature vectors for each candidate image in image database. Conventional algorithms based on exhaustive search are highly time consuming and inefficient. With rapid increase in database size, there is a growing need of a fast and efficient retrieval system. This paper describes a novel & effective approach to Content Based Image Retrieval (CBIR) that represent each image in database by a vector of feature values called "Moments of Pixel Distribution of BMP Image for CBIR". Here we propose a simple and effective approach that can be easily implemented in a programming language. In this technique we are tacking first, second and third order moment of pixel distribution of BMP Image. We obtain compact feature vector of size 36 that create image signature in term of both texture & color. We use simple Euclidean distance to compute the similarity measures of images for Content Based Image Retrieval application. This technique gives acceptable results in a simple and fast way.