Image retrieval is still an active research topic in the computer vision field. There are existing several techniques to retrieve visual data from large databases. Bag-of-Visual Word (BoVW) is a visual feature descriptor that can be used successfully in Content-based Image Retrieval (CBIR) applications. In this paper, we present an image retrieval system that uses local feature descriptors and ...

Given the explosive growth of the Web images, image search plays an increasingly important role in our daily lives. The visual representation of image is the fundamental factor to the quality of content-based image search. Recently, bag-of-visual word model has been widely used for image representation and has demonstrated promising performance in many applications. In the bag-of-visual-word mo...

Local feature coding has drawn much attention in recent years. Many excellent coding algorithms have been proposed to improve the bag-of-words model. This paper proposes a new local feature coding method called local hypersphere coding (LHC) which possesses two distinctive differences from traditional coding methods. Firstly, we describe local features by the edges between visual words. Secondl...

The Bag-of-words framework is probably one of the best models used in image classification. In this model, coding plays a very important role in the classification process. There are many coding methods that have been proposed to encode images in different ways. The relationship between different codewords is studied, but the relationship among descriptors is not fully discovered. In this work,...

This paper describes the participation of the Web Science Laboratory of Meiji University in the ImageCLEF2010 photo annotation task. We devised a system that combines conceptual fuzzy sets with visual words that have become popular in the fields of image retrieval and recognition. The utility of the system was verified by comparing it with the ordinary Bag of Visual words technique. In addition...

This paper presents a novel approach to incorporate spatial information in the bag-of-visual-words (BoVW) model [1, 3] for category level and scene classification. In the traditional BoVW model, feature vectors are histograms of visual words. This representation is appearance based and does not contain any information regarding the arrangement of the visual words in the 2D image space. In this ...

Content-based medical image retrieval (CBMIR) is an active research area for disease diagnosis and treatment but it can be problematic given the small visual variations between anatomical structures. We propose a retrieval method based on a bag-of-visual-words (BoVW) to identify discriminative characteristics between different medical images with Pruned Dictionary based on Latent Semantic Topic...

In original Correspondence LDA (Corr-LDA) [1] the image region is generated by a multivariate Gaussian distribution. We replace this Gaussian distribution by a multinomial distribution, which gives a modified algorithm for parameter estimation. 1 Corr-LDA with Multinomial Generative Process To keep the consistence, we denote visual word and auditory word as sensory word. Thus, A document (a cap...

The Oxford team participated in the high-level feature extraction and interactive search tasks. A vision only approach was used for both tasks, with no use of the text or audio information. For the high-level feature extraction task, we used two different approaches, both based on sparse visual features. One used a standard bag-of-words representation, while the other additionally used a lower-...