As the number of images grows rapidly on the Internet, the scalability of image retrieval systems becomes a significant issue. In this paper, we propose two distributed clustering algorithms to scale up the bag-ofvisual-words model on millions of images and billions of visual features by leveraging distributed systems. We also introduce a multimodal fusion model to utilize textual data to impro...

Document image retrieval is a task to retrieve document images relevant to a user’s query. Most of existing methods based on word-level indexing rely on the representation called “bag of words” which originated in the field of information retrieval. This paper presents a new representation of documents that utilizes additional information about the location of words in pages so as to improve th...

This research work is to develop an efficient and powerful medical search engine to classify and search the radiographic medical images. It focuses on bag of visual words image representation and a similarity matching technique to represent match and retrieve the similar images. This work addresses the issues in content based image retrieval for medical images. In this system can handles differ...

This paper presents a “bag of keypoints” based biomedical image retrieval approach by detecting affine covariant regions. The covariant regions simply refers to a set of pixels or interest points which are invariant to affine transformations, as well as occlusion, lighting and intra-class variations. To describe the intensity pattern within the interest points the Scale-Invariant Feature Transf...

Introduction. With the widespread adoption of laparoscopic and robotic surgery, more and more women are undergoing minimally invasive surgery for complex gynecological procedures. The rate-limiting step is often the delivery of an intact uterus or an unruptured adnexal mass. To avoid conversion to a minilaparotomy for specimen retrieval, we describe a novel technique using an Anchor Tissue Retr...

This master’s theses addressed two research issues in order to investigate and to propose effective solutions for image retrieval on mobile devices: 1) low-cost representation for mobile image search and 2) spatial visual feature extraction. First, we have tested twenty mid-level representations of binary descriptors, ten color descriptors, five texture descriptors and two shape descriptors in ...

This work explores the effect of text representation techniques on the overall performance of medical text classification. To accomplish this goal, we developed a text classification system that supports the very basic word representation (bag-of-words) and the more complex medical phrase representation (bag-of-phrases). We also combined word and phrase representations (hybrid) for further anal...

in this paper, a motion retrieval system is investigated from a multiple-instance learning view. To retrieve similar motion data, each human joint’s motion clip is regarded as a bag, while each of its segments is regarded as an instance. First 3D temporal-spatial features and their keyspaces of each human joint are extracted. Then, data driven decision trees based on ensemble multiple instance ...

With recent popularity of 3D models, retrieval and recognition of 3D models based on their shape has become an important subject of study. This paper proposes a 3D model retrieval algorithm that is invariant to global deformation as well as to similarity transformation of 3D models. The algorithm is based on a set of local 3D geometrical features combined with bag-of-features approach. The algo...

With the advances in both stable interest region detectors and robust and distinctive descriptors, local featurebased image or object retrieval has become a popular research topic. The other key technology for image retrieval systems is image representation such as the bag-of-visual words (BoVW), Fisher vector, or Vector of Locally Aggregated Descriptors (VLAD) framework. In this paper, we revi...