Image Retrieval Based On Color and Texture Features of the Image Sub-blocks

Kavitha, C.; Rao, B. Prabhakara; Govardhan, A.

doi:10.5120/1958-2619

Cited by 36 publications

(12 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…H, S, and V represent hue, saturation and value, respectively. Then, we apply the HSV histogram (Kavitha et al, 2011) to describe the colour information of the WCE images. After dividing H into eight parts, while S and V into three parts, respectively, we get a one-dimensional feature vector, which quantises the whole colour space into 72 kinds of main colours.…”

Section: Colour Featurementioning

confidence: 99%

Hierarchical key frame extraction for wireless capsule endoscopy video based on the saliency map

Yuan

Meng

2014

IJMA

View full text Add to dashboard Cite

Wireless capsule endoscopy (WCE) is a non-invasive imaging technique to examine the entire small intestine. This paper presents a novel hierarchical key frame extraction algorithm based on the saliency map to automatically select a small number of key informative frames in the WCE video to assist the diagnostic process. We start from calculating the saliency map and extracting the inside colour, texture and shape features. We then compute the image information entropies with adapted weights on three features; calculate the differences of entropies in the successive frames and extract the first level key frames to divide the video into multi-segments. Finally, the mean shift clustering method is applied on each segment to obtain the final key frames. The proposed technique achieves the fidelity of 95.07% and the compression ratio of 87.9% on average, validating that the proposed scheme is highly promising for the key frame extraction in the WCE video.Reference to this paper should be made as follows: Yuan, Y. and Meng, M.Q-H. (2014) 'Hierarchical key frame extraction for wireless capsule endoscopy video based on the saliency map', Int.

show abstract

Section: Colour Featurementioning

confidence: 99%

Hierarchical key frame extraction for wireless capsule endoscopy video based on the saliency map

Yuan

Meng

2014

IJMA

View full text Add to dashboard Cite

show abstract

“…The mainstream of visual‐based retrieval takes an approach to divide an image into grid‐shaped blocks and use the similarity between two images based on a comparison of image features in corresponding blocks of the two images (1‐to‐1‐block method) . However, as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

Visual‐based and object‐conscious image retrieval by block reallocation into object region

Mochizuki¹,

Kawai²,

Sano³

et al. 2016

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

Visual-based' image retrieval based on the visual similarities over the entire image is one of the powerful and useful ways when targeting large volume content with inadequate annotation. Generally, conventional methods divide a query image and target images in the database into grid-shaped blocks and calculate the similarity based on image features by comparing each corresponding block straightforwardly. However, the method sometimes fails in terms of object-conscious retrieval when their backgrounds are almost the same but the only the object is different or the object's size and/or position is different. To solve the problem, we propose a new method featuring the reallocation of some blocks to the object region (OB-blocks) and a new similarity score by placing a weight on the OB-blocks, which are derived from the visual saliency map. Our proposed method could realize 'visual-based and object-conscious' image retrieval. We verify the effectiveness of this method through comparison experiments.

show abstract

“…The proposed approach is based on combined features of image sub-blocks and gray-level co-occurrence matrix (GLCM) [11][12][13][14][15][16]. Similar to these methods, the image in proposed method is partitioned into sub-blocks which are of equal size and not coinciding with each other.…”

Section: Proposed Systemmentioning

confidence: 99%

“…Precision is defined as the ratio of the number of retrieved relevant images to the total number of retrieved images [11][12][13][14][15][16]. We denote precision by P and it is computed as follows:…”

Section: Performance Evaluation Metrics For Cbir Systemsmentioning

confidence: 99%

See 1 more Smart Citation

Sub-block Features Based Image Retrieval

Sajwan

Goyal

2014

Computational Intelligence in Data Mining - Volume 1

View full text Add to dashboard Cite

In various domains like security, education, biomedicine etc., the volume of digital data is increasing rapidly, and this is becoming a challenge to retrieve the information from the storage media. Content-based image retrieval systems (CBIR) aim at retrieving from large image databases the images similar to the given query image based on the similarity between image features. This paper aim to discuss and solve the problem of designing sub-block features based image retrieval. Firstly, this paper outlines a description of the primitive features of an image. Then, the proposed methodology for partitioning the image and extracting its colour and texture is described. The algorithms used to calculate the similarity between extracted features, are then explained. Finally, we compared with some other existing CBIR methods, using the WANG database, which is widely used for CBIR performance evaluation, and the results demonstrate the proposed approach outperforms other existing methods considered.

show abstract

Image Retrieval Based On Color and Texture Features of the Image Sub-blocks

Cited by 36 publications

References 18 publications

Hierarchical key frame extraction for wireless capsule endoscopy video based on the saliency map

Hierarchical key frame extraction for wireless capsule endoscopy video based on the saliency map

Visual‐based and object‐conscious image retrieval by block reallocation into object region

Sub-block Features Based Image Retrieval

Contact Info

Product

Resources

About