In multi-instance learning, the training examples are bags composed of instances without labels and the task is to predict the labels of unseen bags through analyzing the training bags with known labels. In content-based image retrieval (CBIR), the query is ambiguous because it is hard to ask the user precisely specify what he or she wants. Such kind of ambiguity can be gracefully dealt with by...

Summary This paper discusses a theory and proposed design for a retrieval artifact using a bag of words (BOW) approach for terms and a standard deviation method for assigning weights. The paper is organized into three parts. The first part is an historical overview of the development of text mining techniques. It is intended to acquaint the reader with our perspectives and assumptions; it is no...

The bag of words representation (BoW), which is widely used in information retrieval (IR), represents documents and queries as word lists that do not express anything about context information. When we look for information, we find that not everything is explicitly stated in a document, so context information is needed to understand its content. This paper proposes the use of bag of concepts (B...

This paper presents a new framework for visual place recognition that incrementally learns models of each place and offers adaptability to dynamic elements in a scene. Traditional bag-of-features image-retrieval approaches to place recognition treat images in a holistic manner and are typically not capable of dealing with sub-scene dynamics, such as structural changes to a building facade or th...

Although it is not presently possible in any system, the style of retrieval described here combines familiar components—co-citation linkages of documents and TF*IDF weighting of terms—in a novel way that could be implemented in citation-enhanced digital libraries of the future. Rather than entering keywords, the user enters a string identifying a work, called a seed, to retrieve the strings ide...

Multi-instance Multi-label learning (MIML) problem is that, given a bag of instances and a set of labels, the task is to assign labels to the bag, which are relevant to the bag as a whole. The problem of MIML finds its relevance in relation extraction, vision, machine learning and information extraction. In this piece of work, our aim is to develop a scalable deep learning approach towards the ...

This paper describes the participation of UNICAEN/GREYC to the ImageCLEF 2011 photo annotation task. The proposed approach uses visual image features and binary annotations of concepts only. In this approach, the annotations are predicted by SVM classifiers trained separately for each concept. The classifiers take Bag-of-Words histograms and fisher vectors representations as inputs, both being ...

How individuals perceive music is influenced by many different factors. The audible part of a piece of music, its sound, does for sure contribute, but is only one aspect to be taken into account. Cultural information influences how we experience music, as does the songs’ text and its sound. Next to symbolic and audio based music information retrieval, which focus on the sound of music, song lyr...

In this paper we have presented a technique for enhancement and retrieval of historic inscription images. Inscription images in general have no distinction between the text layer and background layer due to absence of color difference and possess highly correlated signals and noise; pertaining to which retrieval of such images using search based on feature matching returns inaccurate results. H...