semantic-based image retrial in the vq compressed domain using image annotation statistical models

Semantic-Based Image Retrial in the VQ Compressed Domain using Image Annotation Statistical Models

Compressed-Sampling-Based Image Saliency Detection in the Wavelet Domain

When watching natural scenes, an overwhelming amount of information is delivered to the Human Visual System (HVS). The optic nerve is estimated to receive around 108 bits of information a second. This large amount of information can’t be processed right away through our neural system. Visual attention mechanism enables HVS to spend neural resources efficiently, only on the selected parts of the...

Fast image search on a VQ compressed image database

A fast and efficient image search method is developed for a compressed image database using vector quantization (VQ). An image search on an image database requires an exhaustive sequential scan of all the images, given the similarity measure. If compressed images are dealt with, images are decompressed as an initial operation and then the previously mentioned exhaustive search is performed usin...

Compressed Image Hashing using Minimum Magnitude CSLBP

Image hashing allows compression, enhancement or other signal processing operations on digital images which are usually acceptable manipulations. Whereas, cryptographic hash functions are very sensitive to even single bit changes in image. Image hashing is a sum of important quality features in quantized form. In this paper, we proposed a novel image hashing algorithm for authentication which i...

Semiautomatic Image Retrieval Using the High Level Semantic Labels

Content-based image retrieval and text-based image retrieval are two fundamental approaches in the field of image retrieval. The challenges related to each of these approaches, guide the researchers to use combining approaches and semi-automatic retrieval using the user interaction in the retrieval cycle. Hence, in this paper, an image retrieval system is introduced that provided two kind of qu...

Compressed Domain Image Retrieval Using JPEG2000 and Gaussian Mixture Models

We describe and compare three probabilistic ways to perform Content Based Image Retrieval (CBIR) in compressed domain using images in JPEG2000 format. Our main focus are arbitrary non-uniformly textured color images, as can be found, e.g., in home user image collections. JPEG2000 offers data that can be easily transferred into features for image retrieval. Thus, when converting images to JPEG20...