The bag-of-words (BoW) method has been used widely in language modelling and information retrieval. A document is expressed as a group of words disregarding the grammar and the order of word information. A typical BoW method is latent semantic analysis (LSA), which maps the words and documents onto the vectors in LSA space. In this paper, the concept of BoW is extended to Bag-of-Word Pairs (BoW...

In order to solve the problem of image color recognition, this paper proposes a method recognition and optimization based on deep learning designs postprocessing framework word bag model (bow). The uses CNN features calculates feature similarity. sets with high similarity are input into classifier trained by bow clustering as preliminary retrieval results. results categories largest number imag...

Feature learning and deep learning have drawn great attention in recent years as a way of transforming input data into more effective representations using learning algorithms. Such interest has grown in the area of music information retrieval (MIR) as well, particularly in music audio classification tasks such as auto-tagging. In this paper, we present a twostage learning model to effectively ...

In content-based image retrieval (CBIR), the user usually poses several labelled images and then the system attempts to retrieve all the images relevant to the target concept defined by these labelled images. It may be helpful if the system can return relevant images where the regions of interest (ROI) are explicitly located. In this paper, this task is accomplished with the help of multi-insta...

Current learning to rank approaches commonly focus on learning the best possible ranking function given a small fixed set of documents. This document set is often retrieved from the collection using a simple unsupervised bag-of-words method, e.g. BM25. This can potentially lead to learning a sub-optimal ranking, since many relevant documents may be excluded from the initially retrieved set. In ...

Many traditional information retrieval (IR) tasks, such as text search, text clustering or text categorization, have natural language documents as their first-class objects, in the sense that the algorithms that are meant to solve these tasks require explicit internal representations of the documents they need to deal with. In IR documents are usually given as extensional vectorial representati...

In traditional document clustering models, a document is considered as a bag of words. In this paper we present a new method for generating feature vectors, using the sentence fragments that are called logical terms and statements, in PLIR system. PLIR is a Knowledge-Based Information system based on the theory of the Plausible Reasoning. We have conducted a number of experiments using OHSUMED ...

This study addresses the problem of 3D shape retrieval. While this problem is interesting and emerging as the size of 3D object databases grows rapidly, the main two issues the community has to focus on are: computational efficiency of 3D object retrieval and the quality of retrieval results. In this study we deal with the two considerations, especially the first one namely computational effici...