Local neighbourhood‐based robust colour occurrence descriptor for colour image retrieval

Abstract

Content-based image retrieval (CBIR) is demanding accurate with efficient retrieval approaches to index and retrieve the most similar images from the huge image databases. This study introduces a novel local neighbourhood-based robust colour occurrence descriptor (LCOD) to encode the colour information present in the local structure of the image. The colour information is processed in two steps: first, the number of colours is reduced into a less number of shades by quantising the red-green-blue colour space; second, the reduced colour shade information of the local neighbourhood is used to compute the descriptor. A local colour occurrence binary pattern is generated for each pixel of the image by representing each reduced colour shade occurrence in its local neighbourhood using a binary pattern. The descriptor is constructed by summing the local colour occurrence binary patterns of all the pixels in the image. LCOD is tested over the natural and colour texture databases for CBIR and experimental results suggest that LCOD outperforms other state-of-the-art descriptors. The performance of the proposed descriptor is promising in the case of illumination difference, rotation and scaling also and it can be effectively used for accurate image retrieval under various image transformations.

Content-based Image Retrieval Speedup

“…In this experiment, average precisions for method [29] are 69.26% and 69.20%, respectively. These precisions for method [30] are 67.50% and 67.26% which means that precisions are preserved after database reduction. Category [11]-Before database reduction Proposed-After database reduction Fig.…”

“…Database reduction leads to speed up in retrieval phase since retrieving images from large databases needs more time than small databases. To show that how reduced database obtained from the proposed method speed up the retrieval process, two CBIR methods proposed in [29], [30] are implemented and evaluated by the whole database and reduced database. Retrieval time for these cases are shown in Fig.…”

“…The last experiment is done to evaluate CBIR accuracy before and after database reduction by the proposed method. For this task, the accuracy of methods in [29], [30] are compared before and after database reduction in Figs. 6 and 7, respectively.…”

Multichannel Decoded Local Binary Patterns for Content-Based Image Retrieval

“…1(a) first quantizes each channel then merges each quantized channel to form a single channel and form the feature vector over it. Some typical example of this category is Local Color Occurrence Descriptor (LCOD) [18], Rotation and Scale Invariant Hybrid Descriptor (RSHD) [35], Color Difference Histogram (CDH) [38] and Color CENTRIST [19]. LCOD basically quantized the Red, Green and Blue channels of the image and formed a single image by pooling the quantized images and finally computed the occurrences of each quantized color locally to form the feature descriptor [18].…”

“…Some typical example of this category is Local Color Occurrence Descriptor (LCOD) [18], Rotation and Scale Invariant Hybrid Descriptor (RSHD) [35], Color Difference Histogram (CDH) [38] and Color CENTRIST [19]. LCOD basically quantized the Red, Green and Blue channels of the image and formed a single image by pooling the quantized images and finally computed the occurrences of each quantized color locally to form the feature descriptor [18]. Similarly, RSHD computed the occurrences of textural patterns [35] and CDH used the color quantization in its construction process [38].…”