Existing Web image search engines index images by textual descriptions including filename, image caption, surrounding text, etc. However, the textual description available on the Web could be ambiguous or inaccurate in describing the actual image content and some images irrelevant to user’s query are also returned by text-based search engines. In this paper, we propose to integrate the existing...

This paper illustrates how a text-retrieval and an image retrieval system can simply complement each other. In order to show this we first expose the importance of a good architecture for combining text and image search engines. Then, we focus on our image search engine. Finally, we present a series of queries that fusion text and image aspects.

Retrieval by similarity in image databases can be accomplished by comparing features extracted from a sample image to the features extracted from the images in the database. Relevance feedback is a technique of query refinement used in text as well as in image retrieval systems which is based on user selection of the most relevant images among the ones retrieved, whose features are used to modi...

In this paper, we present a method of camera-based document image retrieval for heterogeneous-content document using a multi-layer separating approach. We use Locally Likely Arrangement Hashing (LLAH) extracting text features on the layer which contains text. In addition, we employ a technique of reducing the memory required for storing the hash table. Experiment result show that our approach i...

An image retrieval system is a computer system for browsing, searching and retrieving images from a large database of digital images. Given a textual query in traditional text based image retrieval (TBIR),relevant images are to be re ranked using visual features after the initial text based image search. In this paper, we propose a new bag based re ranking framework for large scale TBIR. We com...

Content-based image retrieval systems have become a reliable tool for many image database applications. There are several advantages of the image retrieval techniques compared to other simple retrieval approaches such as text-based retrieval techniques. This paper proposes a new image retrieval technique that can be used for retrieving color images. The proposed technique is based on a fractal ...

This paper presents a framework for semi-automatic annotation and semantic image retrieval, applied to the sports domain, based upon semantic analysis of both image text captions and visual features of the image. Unstructured text captions of images are analysed in order to extract the concepts and restructure them into a semantic model. SVM classification of the multi-dominant colours and edge...

Image retrieval is the dealing out of looking and retrieving photographs from a massive dataset. As the images grow complex and diverse, retrieval the right images becomes a difficult challenge. For centuries, most of the images retrieval is text-based which means searching is based on those keyword and text generated by human’s creation. On the whole a content based image retrieval (CBIR) syst...

The aim of this document is to describe the methods we used in the Patent Image Classification and Image-based Patent Retrieval tasks of the Clef-IP 2011 track. The patent image classification task consisted in categorizing patent images into predefined categories such as abstract drawing, graph, flowchart, table, etc. Our main aim in participating in this sub-task was to test how our image cat...

In Text Information Extraction (TIE) process, the text regions are localized and extracted from the images. It is an active research problem in computer vision applications. Diversity in text is due to the differences in size, style, orientation, alignment of text, low image contrast and complex backgrounds. The semantic information provided by an image can be used in different applications suc...