Large-scale image retrieval has been shown remarkable potential in real-life applications. The standard approach is based on Inverted Indexing, given images are represented using Bag-of-Words model. However, one major limitation of both Inverted Index and Bag-of-Words presentation is that they ignore spatial information of visual words in image presentation and comparison. As a result, retrieva...

Recently, the bag-of-words (BOW) based image representation is getting popular in object categorization. However, there is no available visual vocabulary and it has to be learned. As to traditional learning methods, the vocabulary is constructed by exploring only one type of feature or simply concatenating all kinds of visual features into a long vector. Such constructions neglect distinct role...

1, 2 Department of Computer Science and Engineering 3 Department of Information Technology Mepco Schlenk Engineering College, Sivakasi Affiliated to Anna University, Chennai, India Abstract—An Extensive growth in software technologies available on Internet results in tampering of images. The main problem in the real world is to determine whether an image is real or forged. In this paper we prop...

State-of-the-art word embeddings, which are often trained on bag-of-words (BOW) contexts, provide a high quality representation of aspects of the semantics of nouns. However, their quality decreases substantially for the task of verb similarity prediction. In this paper we show that using symmetric pattern contexts (SPs, e.g., “X and Y”) improves word2vec verb similarity performance by up to 15...

This paper describes the different steps which lead to the construction of the LIP6 extractive summarizer. The basic idea behind this system is to expand question and title keywords of each topic with their respective cluster terms. Term clusters are found by unsupervised learning using a classification variant of the well-known EM algorithm. Each sentence is then characterized by 4 features, e...