Vector-quantized local features frequently used in bag-ofvisual-words approaches are the backbone of popular visual recognition systems due to both their simplicity and their performance. Despite their success, bag-of-words-histograms basically contain low-level image statistics (e.g., number of edges of different orientations). The question remains how much visual information is lost in quanti...

In this research, we have proposed semantic based image retrieval system to retrieve set of relevant images for the given query image from the Web. We have used global color space model and Dense SIFT feature extraction technique to generate visual dictionary using proposed quantization algorithm. The images are transformed into set of features. These features are used as inputs in our proposed...

This research looks at the eeects of word order and segmentation on translation retrieval performance for an experimental Japanese-English translation memory system. We implement a number of both bag-of-words and word order-sensitive similarity metrics, and test each over character-based and word-based indexing. The translation retrieval performance of each system connguration is evaluated empi...

This paper proposes a data driven approach to perform scene localization in indoor environments. The proposed algorithm named p-BoW is designed to cope with self-repetitive and confusing patterns in indoor environments of any type. The algorithm uses the Visual Bag of Words (BoW) model along with proposed voting scheme to perform scene localization from a database of captured images. In the fir...

In this paper, we address the problem of large scale crossscenario clothing retrieval with semantic-preserving visual phrases (SPVP). Since the human parts are important cues for clothing detection and segmentation, we firstly detect human parts as the semantic context, and refine the regions of human parts with sparse background reconstruction. Then, the semantic parts are encoded into the voc...

Local features have been proved to be effective in image/video semantic analysis. The BOVW (bag of visual words) scheme can cluster local features to form the visual vocabulary which includes an amount of words, where each word is the center of one clustering feature. The vocabulary is used to recognize the image semantic. In this paper, a new scheme to construct semantic-binding hierarchical v...

We present the Siamese Continuous Bag of Words (Siamese CBOW) model, a neural network for efficient estimation of highquality sentence embeddings. Averaging the embeddings of words in a sentence has proven to be a surprisingly successful and efficient way of obtaining sentence embeddings. However, word embeddings trained with the methods currently available are not optimized for the task of sen...

Categorical compositional distributional models unify compositional formal semantic models and distributional models by composing phrases with tensor-based methods from vector representations. For the tensor-based compositions, Milajevs et al. (2014) showed that word vectors obtained from the continuous bag-of-words (CBOW) model are competitive with those from co-occurrence based models. Howeve...

Automatically classifying the tissues types of Region of Interest (ROI) in medical imaging has been an important application in Computer-Aided Diagnosis (CAD), such as classification of breast parenchymal tissue in the mammogram, classify lung disease patterns in High-Resolution Computed Tomography (HRCT) etc. Recently, bag-of-features method has shown its power in this field, treating each ROI...