# Image Representation Using Stacked Colour Histogram
## 1 Introduction  
The success of the Content-Based Image Retrieval (CBIR) system hinges mainly on the efficacy of the descriptors used to represent images. Colour, shape and texture are widely used image descriptors [1]. Most CBIR methods depend to a large extent on the colour and texture of the image. Colour Histogram is the easiest method to use to represent the colour features for a CBIR task [2, 3]. A comprehensive survey on colour histogram image retrieval is given in [4, 5, 6, 7, 8, 9, 10]. The most significant advantages of the colour histogram are the simplicity [10], fast computation [11] and robustness to rotations and transformations on the image [12]. However, it cannot handle spatial information of colours [13] in an image and therefore texture features extraction techniques are widely used to essentially extract this information from an image [14, 15].  
Texture descriptors are significant for bridging the semantic gaps in CBIR. Local binary patterns-based descriptors (LBP) [16, 17], Gray-Level Co-occurrence Matrix (GLCM) [18], Gabor filter [14, 18] and Hidden Markov random field [19] are widely used to extract textureinformation for the indexing of images. Texture descriptors have not always yielded optimal results; hence, combining them with other descriptors for effective CBIR is common practice. Combining colour and texture in a single descriptor is common even though shapes, colours, edge orientation and texture combination can also be found. Using the combination may employ either the fusion of feature vectors into one feature vector or the feature extraction approach having the inherent nature of extracting two or more features into a single image representation vector. The authors of [20] fused colour feature information and pixel orientation into an image representation vector, while [20; 21; 22; 23; 24; 25] integrated both the colour and texture information in a single descriptor using textons. Textons are structural elements used to capture the run lengths in an image at a particular orientation. Textons generally produce good results. However, the structure of textons is somehow rigid, which sometimes leads to poor performance when the orientation of the images changes [26]. The changes also make the texton feature extractions produce different feature maps or vectors for the same image. Again, feature extraction approaches such as the bag-of-words, edge histograms, and GLCM incorporating neighbourhood or spatial information to extract feature information may be complicated to implement or too rigid at some point.  
This paper proposes a new feature extraction technique that extracts colour and neighbourhood information into a descriptor for indexing images. The presented scheme sums several histograms generated from the transformation of images.  
The contributions of this work are summarised as follows: (a) The proposed SCH method for an image representation vector can effectively deal with texture and colour heterogeneous images; (b) In the SCH method, the inherent colour extraction and neighbourhood information based on recurrent transformations provide more discrimination of colour and texture features; (c) The novel descriptor improves the image recognition rate with minimal memory space and retrieval time.  
Section 2 in this paper presents related works that are relevant to our own. Section 3 introduces our proposed SCH method. Section 4 contains the experimental results, evaluations and analysis of the results and the last section concludes this work.