A texture based image retrieval approach using Self-Organizing Map pre-classification

Rahimi, Mostafa; Mohsen, Ebrahimi Moghadam

doi:10.1109/isspit.2011.6151598

Cited by 3 publications

(6 citation statements)

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“…Regular textures are the scanning result of images by the Texton component [39]. While the DCTon can be considered as the extended version of the Texton [38]. In the approach, the color connectivity regions of an image are considered as the proper properties for extract features that contain the color and texture information simultaneously.…”

Section: ) Low-level Featuresmentioning

confidence: 99%

“…In the approach, the color connectivity regions of an image are considered as the proper properties for extract features that contain the color and texture information simultaneously. If are the color components, and the DCTon components are utilized to describe a pixel with components ( ) (i.e., it appears a specified spatial relationship by considering distances and orientations of the corresponding pixel with components ( ) for each pixel of the image) [38]. After extracting the DCTon components, the values of pixels are set to their average in order to make the DCTon image.…”

Section: ) Low-level Featuresmentioning

confidence: 99%

“…After creating the DCTon image, the Co-occurrence matrixes are extracted from this image. The Co-occurrence matrix of this quantized component is established and its contrast, correlation, energy, and homogeneity features are extracted by a vector which can be defined as follows: [ ] Another low-level features extraction method operates based on the Dual Tree-CWT and SVD (Support Vector Machine), and conceptual segmentation, which was introduced in [38]. These features extracted from n-levels decomposition of 2-D, DT CWT of a W×H input image.…”

Section: ) Low-level Featuresmentioning

confidence: 99%

“…These features are attributes that describes more specific individual components (e.g., color, shape, texture and background) of an image by focusing on the details of rudimentary micro information of images [15]. In this paper, we inspired the lowlevel features from the previous works in order to express the color and texture information of the image [37], [38].…”

Section: ) Low-level Featuresmentioning

confidence: 99%

“…Another low-level features extraction method operates based on the Dual Tree-CWT and SVD (Support Vector Machine), and conceptual segmentation, which was introduced in [38]. These features extracted from n-levels decomposition of 2-D, DT CWT of a W×H input image.…”

Section: ) Low-level Featuresmentioning

confidence: 99%

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An Effective Automatic Image Annotation Model Via Attention Model and Data Equilibrium

Vatani¹,

Ahvanooey²,

Rahimi³

2018

ijacsa

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Nowadays, a huge number of images are available. However, retrieving a required image for an ordinary user is a challenging task in computer vision systems. During the past two decades, many types of research have been introduced to improve the performance of the automatic annotation of images, which are traditionally focused on content-based image retrieval. Although, recent research demonstrates that there is a semantic gap between content-based image retrieval and image semantics understandable by humans. As a result, existing research in this area has caused to bridge the semantic gap between low-level image features and high-level semantics. The conventional method of bridging the semantic gap is through the automatic image annotation (AIA) that extracts semantic features using machine learning techniques. In this paper, we propose a novel AIA model based on the deep learning feature extraction method. The proposed model has three phases, including a feature extractor, a tag generator, and an image annotator. First, the proposed model extracts automatically the high and low-level features based on dual tree continues wavelet transform (DT-CWT), singular value decomposition, distribution of color ton, and the deep neural network. Moreover, the tag generator balances the dictionary of the annotated keywords by a new logentropy auto-encoder (LEAE) and then describes these keywords by word embedding. Finally, the annotator works based on the long-short-term memory (LSTM) network in order to obtain the importance degree of specific features of the image. The experiments conducted on two benchmark datasets confirm that the superiority of proposed model compared to the previous models in terms of performance criteria. Keywords-Automatic image annotation; attention model; skewed learning; deep learning, word embedding; log-entropy auto encoderI.

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Section: ) Low-level Featuresmentioning

confidence: 99%

Section: ) Low-level Featuresmentioning

confidence: 99%

Section: ) Low-level Featuresmentioning

confidence: 99%

Section: ) Low-level Featuresmentioning

confidence: 99%

Section: ) Low-level Featuresmentioning

confidence: 99%

See 3 more Smart Citations

An Effective Automatic Image Annotation Model Via Attention Model and Data Equilibrium

Vatani¹,

Ahvanooey²,

Rahimi³

2018

ijacsa

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Image texture classification and retrieval using self-organizing map

Thakare

Patil

2014

2014 International Conference on Information Systems and Computer Networks (ISCON)

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Content‐based image retrieval based on binary signatures cluster graph

2017

Expert Systems

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In this paper, we approach a method of clustering binary signature of image in order to create a clustering graph structure for building the content‐based image retrieval. First, the paper presents the segmentation method based on low‐level visual features including colour and texture of image. On the basis of segmented image, the paper creates binary signature to describe location, colour, and shape of interest objects. In order to match similar images, the paper presents a similarity measure between the images based on binary signature. From that, the paper proposes the method of clustering binary signature to quickly query similar images. At the same time, the graph data structure is built using the partition cluster technique and the rules of binary signatures' distribution of images. On the basis of data structure, we propose a graph creation algorithm, a cluster splitting/merging algorithm, and a similarity image retrieval algorithm. To illustrate the proposed theory, we build an image retrieval application and assess the experimental results on the image datasets including COREL (1,000 images), CBIR images (1,344 images), WANG (10,800 images), MSRDI (15,720 images), and ImageCLEF (20,000 images).

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A texture based image retrieval approach using Self-Organizing Map pre-classification

Cited by 3 publications

References 10 publications

An Effective Automatic Image Annotation Model Via Attention Model and Data Equilibrium

An Effective Automatic Image Annotation Model Via Attention Model and Data Equilibrium

Image texture classification and retrieval using self-organizing map

Content‐based image retrieval based on binary signatures cluster graph

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