An efficient image retrieval scheme for colour enhancement of embedded and distributed surveillance images

Abstract

a b s t r a c tFrom the past few years, the size of the data grows exponentially with respect to volume, velocity, and dimensionality due to wide spread use of embedded and distributed surveillance cameras for security reasons. In this paper, we have proposed an integrated approach for biometric-based image retrieval and processing which addresses the two issues. The first issue is related to the poor visibility of the images produced by the embedded and distributed surveillance cameras, and the second issue is concerned with the effective image retrieval based on the user query. This paper addresses the first issue by proposing an integrated image enhancement approach based on contrast enhancement and colour balancing methods. The contrast enhancement method is used to improve the contrast, while the colour balancing method helps to achieve a balanced colour. Importantly, in the colour balancing method, a new process for colour cast adjustment is introduced which relies on statistical calculation. It adjusts the colour cast and maintains the luminance of the image. The integrated image enhancement approach is applied to the enhancement of low quality images produced by surveillance cameras. The paper addresses the second issue relating to image retrieval by proposing a content-based image retrieval approach. The approach is based on the three features extraction methods namely colour, texture and shape. Colour histogram is used to extract the colour features of an image. Gabor filter is used to extract the texture features and the moment invariant is used to extract the shape features of an image. The use of these three algorithms ensures that the proposed image retrieval approach produces results which are highly relevant to the content of an image query, by taking into account the three distinct features of the image and the similarity metrics based on Euclidean measure. In order to retrieve the most relevant images, the proposed approach also employs a set of fuzzy heuristics to improve the quality of the results further. The results show the proposed approaches perform better than the well-known existing approaches.

Big data analytics: Computational intelligence techniques and application areas

“…Surveillance cameras are used to monitor traffic flow, threats, and suspicious activities in order to support government and law-enforcement agencies. They play a vital role in security issues, as they are widely deployed in public places to prevent or investigate crimes and to detect a suspicious person in order to make the environment more secure (Iqbal et al, 2015a, Iqbal et al, 2015b. Computational intelligence methods can help to classify physiological and behavioural traits to effectively identity suspicious and abnormal behaviour for various applications such as vehicle theft, crowd surveillance through monitoring and analysis of multiple real-time data streams.…”

“…In order to harvest the advantages of Big Data analytics in an increasingly knowledge driven society there is a need to develop solutions that reduce the complexity and cognitive burden on accessing and processing these large volumes of data in both embedded hardware and software based data analytics (Maniak et al, 2015;Iqbal et al, 2015a, Iqbal et al, 2015b. Big challenges stem from the utilization of Big Data in real world, since the implementation of real time applications is becoming increasingly complex.…”

Greedy Learning of Deep Boltzmann Machine (GDBM)’s Variance and Search Algorithm for Efficient Image Retrieval

“…Reference [10] proposed an image retrieval approach using a mixture of texture and shape features. Fuzzy C-means clustering and k-means clustering were employed to segment the images.…”

A Review on Intelligent Monitoring and Activity Interpretation

“…Furthermore, the distribution of high-level information between sensors also poses a problem, as objects may pass through the coverage areas of different sensors, which requires the existence of mechanisms that ensure the consistency of data. Also, the size of data managed by networked surveillance systems grows exponentially with respect to volume, velocity, and dimensionality, due to the widespread use of embedded surveillance cameras and sensors [41].…”