Michal Batko

Followers

Following

Co-authors

Public Views

Alireza Noruzi

University of Tehran

Aswani Kumar Cherukuri

VIT University

Alexander Arzamastsev

Tambov State University

Kati (Katalin) Prajda

University of Vienna

Lev Manovich

Graduate Center of the City University of New York

Carlo Bianchini

University of Pavia

Samia C H E H B I Gamoura

Université de Strasbourg

Andres Garcia-Camino

CSIC (Consejo Superior de Investigaciones Científicas-Spanish National Research Council)

Maria Teresa Biagetti

Università degli Studi "La Sapienza" di Roma

Mohamad Ivan Fanany

University of Indonesia

Interests

Uploads

Papers by Michal Batko

Fusion Strategies for Large-Scale Multi-modal Image Retrieval

Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXIII

Advances in Database Systems

Download

Similarity Search in Large Collections of Biometric Data

... 8 pages, 2008. Paolo Bolettieri, Andrea Esuli, Fabrizio Falchi, Claudio Lucchese, RaffaelePer... more

GHT*: a peer-to-peer system for metric data

GHT* is a scalable and distributed similarity search structure that has been specifically designe... more GHT* is a scalable and distributed similarity search structure that has been specifically designed to support metric space objects. Our structure is based on the P2P communication paradigm and it is scalable in that it distributes the data over more and more independent peer computers. By exploiting parallelism in a dynamic network of computers, the query execution scales up very well considering both the number of distance computations and the hop count between the peers. Updates are performed locally and a node splitting never ...

Scalable and Distributed Similarity Search in Metric Spaces

In this paper we propose a new access structure, called GHT*, based on generalized hyperplane tre... more In this paper we propose a new access structure, called GHT*, based on generalized hyperplane tree (GHT) and distributed dynamic hashing (DDH) techniques. GHT* is a distributed structure which allows to perform range search in a metric space according to a distance function d. The structure does not require a central directory and it is able to gracefully scale through

Download

Content Based Image Retrieval on the Web

It has become customary that practically any information can be in digital form. Searching throug... more It has become customary that practically any information can be in digital form. Searching through future internet will be complicated because of: (1) the diversity of ways in which specific data can be sorted, compared, related, or clas- sified, and (2) the exponentially increasing amount of digital data. Accordingly, a successful search engine should address problems of extensibility and scalability.

Download

A P2P-based System for searching in Metric Spaces

Sistemi Evoluti per Basi di Dati, 2004

MUFIN at ImageCLEF 2011: Success or Failure?

In all fields of research it is important to discuss and compare various methods that are being p... more In all fields of research it is important to discuss and compare various methods that are being proposed to solve given problems. In image retrieval, the ImageCLEF competitions provide such comparison platform. We have participated in the Photo Annotation Task of the ImageCLEF 2011 competition with a system based on the MUFIN Annotation Tool. Our approach is, in contrast to typical classifier solutions, based on a general annotation system for web images that provides general keywords for arbitrary image. However, the free-text annotation needs to be transformed into the 99 concepts given by the competition task. The transformation process is described in detail in the first part of this paper. In the second part, we discuss the results achieved by our solution. Even though the free-text annotation approach was not as successful as the classifier-based approaches, the results are competitive especially for the concepts involving real-world objects. On the other hand, our approach does not require training and is scalable to any number of concepts.

Download

DISA at ImageCLEF 2014: The search-based solution for scalable image annotation

This paper presents an annotation tool developed by the DISA Laboratory for the ImageCLEF 2014 Sc... more This paper presents an annotation tool developed by the DISA Laboratory for the ImageCLEF 2014 Scalable Concept Image An-notation challenge. Our solution exploits the search-based annotation paradigm and utilizes several sources of semantic information to deter-mine the relevance of candidate concepts. Rather than relying on the quality of training data, our approach profits from the large quantities of information available in large image collections and semantic knowledge bases. The results achieved by our system confirm that this approach is very promising for scalable image annotation.

Download

A Scalable Nearest Neighbor Search in P2P Systems

Lecture Notes in Computer Science, 2005

Similarity search in metric spaces represents an important paradigm for content-based retrieval i... more Similarity search in metric spaces represents an important paradigm for content-based retrieval in many applications. Existing centralized search structures can speed-up retrieval, but they do not scale up to large volume of data because the response time is linearly increasing with the size of the searched file. In this article, we study the problem of executing the nearest neighbor(s) queries in a distributed metric structure, which is based on the P2P communication paradigm and the generalized hyperplane partitioning. By exploiting parallelism in a dynamic network of computers, the query execution scales up very well considering both the number of distance computations and the hop count between the peers. Results are verified by experiments on real-life data sets.

Download

Lecture Notes in Computer Science, 2005

Similarity search in metric spaces represents an important paradigm for content-based retrieval o... more Similarity search in metric spaces represents an important paradigm for content-based retrieval of many applications. Existing centralized search structures can speed-up retrieval, but they do not scale up to large volume of data because the response time is linearly increasing with the size of the searched file. The proposed GHT* index is a scalable and distributed structure. By exploiting parallelism in a dynamic network of computers, the GHT* achieves practically constant search time for similarity range queries in data-sets of arbitrary size. The structure also scales well with respect to the growing volume of retrieved data. Moreover, a small amount of replicated routing information on each server increases logarithmically. At the same time, the potential for interquery parallelism is increasing with the growing data-sets because the relative number of servers utilized by individual queries is decreasing. All these properties are verified by experiments on a prototype system using real-life data-sets.

Download

Visual Image Search: Feature Signatures or/and Global Descriptors

Lecture Notes in Computer Science, 2012

The success of content-based retrieval systems stands or falls with the quality of the utilized s... more The success of content-based retrieval systems stands or falls with the quality of the utilized similarity model. In the case of having no additional keywords or annotations provided with the multimedia data, the hard task is to guarantee the highest possible retrieval precision using only content-based retrieval techniques. In this paper we push the visual image search a step further by testing effective combination of two orthogonal approaches-the MPEG-7 global visual descriptors and the feature signatures equipped by the Signature Quadratic Form Distance. We investigate various ways of descriptor combinations and evaluate the overall effectiveness of the search on three different image collections. Moreover, we introduce a new image collection, TWIC, designed as a larger realistic image collection providing ground truth. In all the experiments, the combination of descriptors proved its superior performance on all tested collections. Furthermore, we propose a re-ranking variant guaranteeing efficient yet effective image retrieval.

Download

Distinct nearest neighbors queries for similarity search in very large multimedia databases

Proceeding of the eleventh international workshop on Web information and data management - WIDM '09, 2009

As the volume of multimedia data available on internet is tremendously increasing, the content-ba... more As the volume of multimedia data available on internet is tremendously increasing, the content-based similarity search becomes a popular approach to multimedia retrieval. The most popular retrieval concept is the k nearest neighbor (kNN) search. For a long time, the kNN queries provided an effective retrieval in multimedia databases. However, as today's multimedia databases available on the web grow to massive volumes, the classic kNN query quickly loses its descriptive power. In this paper, we introduce a new similarity query type, the k distinct nearest neighbors (kDNN), which aims to generalize the classic kNN query to be more robust with respect to the database size. In addition to retrieving just objects similar to the query example, the kDNN further ensures the objects within the result have to be distinct enough, i.e. excluding near duplicates.

Download

Face-Based People Searching in Videos

Lecture Notes in Computer Science, 2013

ABSTRACT We propose a system for retrieving people according to their faces in unannotated video ... more ABSTRACT We propose a system for retrieving people according to their faces in unannotated video streams. The system processes input videos to extract key-frames on which faces are detected. The detected faces are automatically grouped together to create clusters containing snapshots of the same person. The system also facilitates annotation and manual manipulations with created clusters. On the processed videos the system offers to search for persons in three distinct operations applicable to various scenarios. The system is presented online by indexing five high-quality video streams with the total length of nearly five hours.

Distributed and Scalable Similarity Searching in Metric Spaces

Lecture Notes in Computer Science, 2004

... 7. Dejan S. Milojicic, Vana Kalogeraki, Rajan Lukose, Kiran Nagaraja, Jim Pruyne, Bruno Richa... more

Visual video retrieval system using MPEG-7 descriptors

Proceedings of the Third International Conference on SImilarity Search and APplications - SISAP '10, 2010

Video is one of the most popular media these days. However, the volume of the video content grows... more Video is one of the most popular media these days. However, the volume of the video content grows very fast, e.g. about 24 hours of video content is uploaded every minute to the most famous web site YouTube, and the necessity to search in this content is apparent. Generally, most of the video search systems are based on annotations or

Generic similarity search engine demonstrated by an image retrieval application

Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval - SIGIR '09, 2009

We introduce a generic engine for large-scale similarity search and demonstrate it on a set of 10... more

DISA at ImageCLEF 2014 Revised: Search-based Image Annotation with DeCAF Features

This paper constitutes an extension to the report on DISA-MU team participation in the ImageCLEF ... more This paper constitutes an extension to the report on DISA-MU team participation in the ImageCLEF 2014 Scalable Concept Image Annotation Task as published in [3]. Specifically, we introduce a new similarity search component that was implemented into the system, report on the results achieved by utilizing this component, and analyze the influence of different similarity search parameters on the annotation quality.

Download

Towards Fast Multimedia Feature Extraction: Hadoop or Storm

2014 IEEE International Symposium on Multimedia, 2014

The current explosion of data accelerated evolution of various content-based indexing techniques ... more The current explosion of data accelerated evolution of various content-based indexing techniques that allow to efficiently search in multimedia data such as images. However, indexable features must be first extracted from the raw images before the indexing. This necessary step can be very time consuming for large datasets thus parallelization is desirable to speed the process up. In this paper, we experimentally compare two approaches to distribute the task among multiple machines: the Apache Hadoop and the Apache Storm projects.

Download

Improving the image retrieval system by ranking

ABSTRACT With the rapid growth of multimedia data, a lot of attention has been recently devoted t... more ABSTRACT With the rapid growth of multimedia data, a lot of attention has been recently devoted to the development of multimedia retrieval systems. The research has followed two main directions: The first one applies existing text-search mechanisms to retrieve multimedia data based on its descriptive annotations, the second approach retrieves data by content. In case of text-based searching, the quality of results depends on the quality of text metadata, which is often not very high (especially in large general-purpose collections such as web image galleries). In the content-based approach, data objects are indexed and searched using features extracted from the data that describe their important characteristics. However, this solution suffers from the well-known semantic gap problem, i.e. the discrepancy between the similarity as computed using the descriptors and human understanding of similarity.

Fusion Strategies for Large-Scale Multi-modal Image Retrieval

Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXIII