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Abstract
In this work; we address a novel interactive framework for object retrieval using unsupervised similar region merging and flood fill method which models the spatial and appearance relations among image pixels. Efficient and effective image segmentation is usually very hard for natural and complex images. This paper presents a new technique for similar region merging and objects retrieval. The users only need to roughly indicate the after which steps desired objects boundary is obtained during merging of similar regions. A novel similarity based region merging mechanism is proposed to guide the merging process with the help of mean shift technique. A region R is merged with its adjacent regions Q if Q has highest similarity with R among all Q’s adjacent regions. The proposed method automatically merges the regions that are initially segmented through mean shift technique, and then effectively extracts the object contour by merging all similar regions. Extensive experiments are performed on 22 object classes (524 images total) show promising results.
Highlights
CLASS-SPECIFIC object segmentation is one of the fundamental problems in computer vision
We proposed unsupervised similar region merging method based on initial segmentation of mean shift
In this paper proposed a class specific object segmentation method using unsupervised similar region merging and flood fill algorithm
Summary
CLASS-SPECIFIC (or category-level) object segmentation is one of the fundamental problems in computer vision. There has been a substantial amount of research on image segmentation including clustering based methods, region growing methods [5], histogram based methods [6], and more recent one such as adaptive thresh-hold methods [7], level set methods [8], graph based methods [4, 9] etc. Despite many years of research, unsupervised image segmentation techniques without human interaction still do not produce satisfactory results [10]. Semi-supervised segmentation methods incorporating user interactions have been proposed [11, 12, 13, 14, 15] and are becoming more and more popular. In the active contour model (ACM) i.e. snake algorithm [11], a proper selection of initial curve by user lead to a good convergence of the true object contour
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