Texture segmentation by multiscale aggregation of filter responses and shape elements

Galun,; sharon,; Basri, Basri; Brandt, Laura

doi:10.1109/iccv.2003.1238418

Cited by 176 publications

(133 citation statements)

References 13 publications

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“…The computation of PES (i|Ref ) for every pixel i (where Ref is either S or S) involves finding a 'maximal region' R surrounding i which has similar regions elsewhere in Ref , i.e., a region R that maximizes (4). An image region R (of any shape or size) is represented by a dense and structured 'ensemble of patch descriptors' using a star-graph model.…”

Section: Our Resultsmentioning

confidence: 99%

“…We continue to describe our figure-ground segmentation algorithm in Sec. 4. Experimental results are provided in Sec.…”

Section: Fig 3 Notationsmentioning

confidence: 99%

“…The diversity in segment types has led to a wide range of approaches for image segmentation: Algorithms for extracting uniformly colored regions (e.g., [1,2]), algorithms for extracting textured regions (e.g., [3,4]), algorithm for extracting regions with a distinct empirical color distribution (e.g., [5,6,7]). Some algorithms employ symmetry cues for image segmentation (e.g., [8]), while others use high-level semantic cues provided by object classes (i.e., class-based segmentation, see [9,10,11]).…”

Section: Introductionmentioning

confidence: 99%

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What Is a Good Image Segment? A Unified Approach to Segment Extraction

Bagon

Boiman

Irani

2008

Lecture Notes in Computer Science

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Abstract. There is a huge diversity of definitions of "visually meaningful" image segments, ranging from simple uniformly colored segments, textured segments, through symmetric patterns, and up to complex semantically meaningful objects. This diversity has led to a wide range of different approaches for image segmentation. In this paper we present a single unified framework for addressing this problem -"Segmentation by Composition". We define a good image segment as one which can be easily composed using its own pieces, but is difficult to compose using pieces from other parts of the image. This non-parametric approach captures a large diversity of segment types, yet requires no pre-definition or modelling of segment types, nor prior training. Based on this definition, we develop a segment extraction algorithm -i.e., given a single point-ofinterest, provide the "best" image segment containing that point. This induces a figure-ground image segmentation, which applies to a range of different segmentation tasks: single image segmentation, simultaneous co-segmentation of several images, and class-based segmentations.

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Section: Our Resultsmentioning

confidence: 99%

“…We continue to describe our figure-ground segmentation algorithm in Sec. 4. Experimental results are provided in Sec.…”

Section: Fig 3 Notationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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What Is a Good Image Segment? A Unified Approach to Segment Extraction

Bagon

Boiman

Irani

2008

Lecture Notes in Computer Science

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“…Its goal to segment an image into regions with each region solely containing object(s) of a class. As object segmentation requires that each segmented region to be a semantic object, it is much more challenging than traditionally object segmentation [1,2,3,4]. 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.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Method of Object Retrieval Using Similar Region Merging and Flood Fill

Saxena¹,

Jain²,

Singh³

2011

SpecialIssue

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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.

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“…This algorithm has been extended to also handle texture cues [7], although our implementation did not make use of these cues. Below we describe the main principles behind the SWA algorithm (Section 3.1) and how we combine motion with intensity cues in this framework (Section 3.2).…”

Section: Using Intensity Cuesmentioning

confidence: 99%