Fuzzy Clustering With Partial Supervision in Organization and Classification of Digital Images

Pedrycz, Witold; Amato, Alberto; Lecce, Vincenzo Di; Piuri, Vincenzo

doi:10.1109/tfuzz.2008.917287

Cited by 56 publications

(18 citation statements)

References 37 publications

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“…The PRCP values are crossing 0.5 which are showing the achievement of quite better results in CBIR field. Overall observation of PRCP indicates that all modifications are performing better as compared to original as well as Equalized histogram which is actually found most commonly used histogram specification in CBIR [27][28][29][30][31][32][34][35][36].…”

Section: Resultsmentioning

confidence: 99%

Role of Histogram Modification Functions EQH, LOG, POLY, Linear Equations for CBIR Based on 64 Bins Approach

Kekre¹,

Sonawane²

2013

IJAST

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Section: Resultsmentioning

confidence: 99%

Role of Histogram Modification Functions EQH, LOG, POLY, Linear Equations for CBIR Based on 64 Bins Approach

Kekre¹,

Sonawane²

2013

IJAST

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“…They used a purely datadriven scene matching approach. Pedrycz et al proposed a digital image organization and classification method based on fuzzy clustering with partial supervision [35] and showed that the main features of fuzzy clustering are essential in revealing the structure in a collection of images and supporting their classification.…”

Section: Object Recognition Systemsmentioning

confidence: 99%

“…If we classify the images according to their visual features, we can get more homogeneous set of landmark images. There are diverse classifiers for this purpose [2,19,35]. To do landmark image classification, we first compute a feature vector representing the color distribution for each image.…”

Section: Classifying Landmark Imagesmentioning

confidence: 99%

Classification and indexing scheme of large-scale image repository for spatio-temporal landmark recognition

Kim

Rho

Jun

et al. 2015

ICA

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In this paper, we propose a classification and indexing scheme of large-scale image repository for spatio-temporal landmark recognition using the local features, GPS data and user tags of images. For spatio-temporal landmark image classification, we first divide Earth's entire surface into unit grid cells and collect pictures taken in each cell through Flickr. The collected images contain information such as location, titles and other user tags. Usually, the titles or user tags of landmark images include landmark names. Hence, by analyzing such tags, we can identify promising landmark names in the region and create a collection of images for each landmark using Flickr API. Even though each landmark class contains images of the same landmark, their spatio-temporal features could be different depending on shooting time, distance or angle. Therefore, we further divide the images in each landmark class into several subclasses according to their spatio-temporal characteristics using their color and local features. Especially, we detect the interest points of the images in the class, construct their feature descriptors using SURF and perform statistical analysis to select their representative points. Similar representative points are merged for fast comparison. Finally, we construct an index on the representative points using k-d tree. To identify the landmark in a user query image, we extract its SURF features and search for them in the index. Most similar matches are returned, along with descriptive text and GPS information. We implemented a prototype system based on a client-server architecture and performed various experiments to demonstrate that our scheme can achieve reasonable precision and scalability and provide a new browsing experience to the user.

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“…From each MVO two low level visual features (signatures) are extracted: region based color histogram and texture. These features were used in many Content Based Image Retrieval (CBIR) systems [19,20,21].…”

Section: Video Acquisitionmentioning

confidence: 99%

An Image Retrieval Based Solution for Correspondence Problem in Binocular Vision

Amato

Piuri

Lecce

2010

2010 IEEE RIVF International Conference on Computing &Amp; Communication Technologies, Research, Innovation, and Vision for The

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Aim of this paper is to propose a solution to the correspondence problem in multi-camera systems. In these systems, two or more cameras are used to record the same scene from different view points. In this way it is possible to face the problem of occlusions in crowding scenes. In this work an object level motion detection algorithm is used and it is applied to the videos sampled by two cameras.The proposed approach does not require a calibration stage and it does not introduce any constraints about the camera positions. Once that the moving objects are detected, they are characterized using image retrieval techniques.The system was tested using two cameras. Object detection and tracking are primary tasks in automatic video streaming analysis. The obtained results in terms of correct classifications rate seem to be encouraging because they highlight the ability of the system to work also in presence of crowding scenes.

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Fuzzy Clustering With Partial Supervision in Organization and Classification of Digital Images

Cited by 56 publications

References 37 publications

Role of Histogram Modification Functions EQH, LOG, POLY, Linear Equations for CBIR Based on 64 Bins Approach

Role of Histogram Modification Functions EQH, LOG, POLY, Linear Equations for CBIR Based on 64 Bins Approach

Classification and indexing scheme of large-scale image repository for spatio-temporal landmark recognition

An Image Retrieval Based Solution for Correspondence Problem in Binocular Vision

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