Detection of Interest Points for Image Indexation

Bres, Stéphane; Jolion, Jean-Michel

doi:10.1007/3-540-48762-x_53

Cited by 44 publications

(28 citation statements)

References 4 publications

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“…The basic information is still the variation in the stimuli but it is no longer taken into account in a differential way but mainly as an energy point of view [42]. According to [5], our approach joins with the last purpose [10]. The main idea is to exploit a detector based on luminance and variation of the edge orientation.…”

Section: Key-points Detectionmentioning

confidence: 99%

A generic framework for semantic video indexing based on visual concepts/contexts detection

Elleuch

Ammar

Alimi

2014

Multimed Tools Appl

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Providing a semantic access to video data requires the development of concept detectors. However, semantic concepts detection is a hard task due to the large intra-class and the small inter-class variability of content. Moreover, semantic concepts co-occur together in various contexts and their occurrence may vary from one to another. Thus, it is interesting to exploit this knowledge in order to achieve satisfactory performances. In this paper we present a generic semantic video indexing scheme, called SVI_REGIMVid. It is based on three levels of analysis. The first level (level1) focuses on low-level processing such as video shot boundary/ key-frame detection, annotation tools, key-points detection and visual features extraction tools. The second level (level2) aims to build the semantic models for supervised learning of concepts/contexts. The third level (level3) enriches the semantic interpretation of concepts/ contexts by exploiting fuzzy knowledge. The obtained experimental results are promising for a semantic concept/context detection process.

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Section: Key-points Detectionmentioning

confidence: 99%

A generic framework for semantic video indexing based on visual concepts/contexts detection

Elleuch

Ammar

Alimi

2014

Multimed Tools Appl

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“…Hence,the image could be described through the low-level features of the small region around interest points. A good many detectors for interest points have been proposed in the literature [1,2,6,8,10],among which harris corner is one of the most reliable detection techniques.…”

Section: A Interest Points Detectionmentioning

confidence: 99%

A Robust Image Retireval Algorithm Based on Interest points

Wenwen¹,

Sun²

2014

Proceedings of the 3rd International Conference on Computer Science and Service System

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Abstract-The global features can not capture all parts of the image having different characteristics and can not contain the spatial features. so in this work we present a novel approach which extracts the local color and texture features based on interest points and utilizes the local information of the interest points to express the contents of images. It not only overcomes the shortage that single image feature can not be truly characterized, but also has a strong robustness to rotation, translation and criterion. Experiment results demonstrate that the proposed approach shows a quite improvement in the retrieval effectiveness.

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“…pixel locations at which the image signal changes twodimensionally (see e.g. Schmid and Mohr 1997, Bres and Jolion 1999, Loupias and Sebe 2000, Amsaleg and Gros 2001.…”

Section: Visual Featuresmentioning

confidence: 99%

Content-Based Image Retrieval Using Self-Organizing Maps

Laaksonen

Koskela

Oja

1999

Visual Information and Information Systems

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Digital image libraries are becoming more common and widely used as visual information is produced at a rapidly growing rate. Creating and storing digital images is nowadays easy and getting more affordable all the time as the needed technologies are maturing and becoming eligible for general use. As a result, the amount of data in visual form is increasing and there is a strong need for effective ways to manage and process it. In many settings, the existing and widely adopted methods for text-based indexing and information retrieval are inadequate for these new purposes.Content-based image retrieval addresses the problem of finding images relevant to the users' information needs from image databases, based principally on low-level visual features for which automatic extraction methods are available. Due to the inherently weak connection between the high-level semantic concepts that humans naturally associate with images and the low-level visual features that the computer is relying upon, the task of developing this kind of systems is very challenging. A popular method to improve retrieval performance is to shift from single-round queries to navigational queries where a single retrieval instance consists of multiple rounds of user-system interaction and query reformulation. This kind of operation is commonly referred to as relevance feedback and can be considered as supervised learning to adjust the subsequent retrieval process by using information gathered from the user's feedback.In this thesis, an image retrieval system named PicSOM is presented, including detailed descriptions of using multiple parallel Self-Organizing Maps (SOMs) for image indexing and a novel relevance feedback technique. The proposed relevance feedback technique is based on spreading the user responses to local SOM neighborhoods by a convolution with a kernel function. A broad set of evaluations with different image features, retrieval tasks, and parameter settings demonstrating the validity of the retrieval method is described. In particular, the results establish that relevance feedback with the proposed method is able to adapt to different retrieval tasks and scenarios.Furthermore, a method for using the relevance assessments of previous retrieval sessions or potentially available keyword annotations as sources of semantic information is presented. With performed experiments, it is confirmed that the efficiency of semantic image retrieval can be substantially increased by using these features in parallel with the standard low-level visual features.

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Detection of Interest Points for Image Indexation

Cited by 44 publications

References 4 publications

A generic framework for semantic video indexing based on visual concepts/contexts detection

A generic framework for semantic video indexing based on visual concepts/contexts detection

A Robust Image Retireval Algorithm Based on Interest points

Content-Based Image Retrieval Using Self-Organizing Maps

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