Object extraction as a basic process for content-based image retrieval (CBIR) system

Abstract

This article describes the way in which image is prepared for content-based image retrieval system. Automated image extraction is crucial; especially, if we take into consideration the fact that the feature selection is still a task performed by human domain experts and represents a major stumbling block in the process of creating fully autonomous CBIR systems. Our CBIR system is dedicated to support estate agents. In the database, there are images of houses and bungalows. We put all our efforts into extracting elements from an image and finding their characteristic features in the unsupervised way. Hence, the paper presents segmentation algorithm based on a pixel colour in RGB colour space. Next, it presents the method of object extraction applied to obtain separate objects prepared for the process of introducing them into database and further recognition. Moreover, we present a novel method of texture identification which is based on wavelet transformation. Due to the fact that the majority of texture is geometrical (such as bricks and tiles) we have used the Haar wavelet. After a set of low-level features for all objects is computed, the database is stored with these features.

How to compare search engines in CBIR?

“…5. In general, our system consists of five main blocks: the image preprocessing block (28], the classifying unit, the Oracle Database [29], the search engine (30] and the graphical user's interface (GUI). All modules, except the Oracle DBMS, are implemented in Matlab.…”

“…downloaded from the Internet) is segmented, creating a collection of objects. Each object, selected according to the algorithm presented in detail in [28], is described by some low-level features Ji. We collect r = 45 features for each graphical object, for which we construct a feature vector O = {/i,J,, ...…”

The Inner Structure of Database for the CBIR System

“…These features are: colour, area, centroid, eccentricity, orientation, texture parameters, moments of inertia, etc. The segmentation algorithm, object extraction algorithm, as well as texture parameters finding algorithm are presented in detail in an article by Jaworska [9]. Object storage in the DB takes place after counting the object low-level features and their logical features (juxtaposition, for instance) -details in sec.…”

Touchscreen User Interface Design for Content Based Image Retrieval

“…The CBIR-to-art-history application stimulates development of specialised tools and procedures in this field (Otal et al 2008, Automatic Watermark Detection Tool for the Bernstein Project 2008). Some of these are distinguished by great precision, while being time consuming and demanding a higher grade software, while others run faster at the cost of precision (Jaworska 2007, Liu et al 2007). Both semi-automatic and fully automatised methods are applicable.…”